Name: IT: Programming, Web Development, Networking & Security Complete Training
Price: 150 GBP
Availability: InStock

<strong>This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.</strong>
        <p>Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics.

The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement.

By the end of this course, you will have a fundamental knowledge of AI and ML.

All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>What You Will Learn</h2><p>Find out why machine learning (ML) is called a black box<br>Get to grips with the features and labels of ML<br>Uncover techniques for training ML models<br>Find out the differences between supervised and unsupervised learning<br>Understand regression, clustering, and dimension reduction</p>
        <h2>Audience</h2><p>If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.</p>
        <h2>Approach</h2><p>With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).</p>
        <h2>Key Features</h2><p>Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

Packt

Deep Learning

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

Machine Learning for Absolute Beginners - Level 1

General Admission

<p>Imagine extracting meaningful insights from vast amounts of data with just a few lines of code. Imagine visualising complex data sets and presenting them in an understandable format to your colleagues and clients. That's the power of <strong>R Programming for Data Science</strong>.</p><p>R Programming is essential for data scientists, statisticians, and researchers. This programming language lets you manipulate, analyse, and visualise data quickly. It's an open-source language that has gained widespread popularity due to its flexibility, robustness, and statistical capabilities.</p><p>With our course, you will learn the fundamental concepts of <strong>R Programming</strong> and how to use it for data analysis, visualisation, and modelling. You will learn to manipulate data frames, clean and preprocess data, and build predictive models using machine learning algorithms.</p><p>This course is endorsed by the <strong>Quality Licence Scheme</strong>, which means you can be sure that the content is of the highest quality. Learners will receive a <strong>Level 5 Endorsed</strong> certificate hardcopy upon course completion, adding valuable credentials to their resumes.</p><p>And that's not all. We're also giving away five career-guided courses for free with this <strong>QLS </strong>course. You can choose from our Career Development Plan Fundamentals, Networking Skills for Personal Success, Boost Your Confidence and Self-Esteem, CV Writing and Job Searching, or Learn to Reduce and Manage Stress courses.</p><p>So why wait? Sign up for our R Programming for Data Science course today and unlock the power of data analysis with R programming.</p><p><strong>This Bundle Comprises The Following Courses:</strong></p><p><strong>QLS Endorsed Course:</strong></p><ul><li><strong>Course 01:</strong> Diploma in R Programming for Data Science at QLS Level 5</li></ul><p><strong>CPD QS Accredited Courses:</strong></p><ul><li><strong>Course 01:</strong> Career Development Plan Fundamentals</li><li><strong>Course 02:</strong> Networking Skills for Personal Success</li><li><strong>Course 03:</strong> Boost Your Confidence and Self-Esteem</li><li><strong>Course 04:</strong> CV Writing and Job Searching</li><li><strong>Course 05:</strong> Learn to Reduce and Manage Stress</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Understand the fundamentals of data science and the R programming language.</li><li>Familiarise with RStudio and the R programming environment.</li><li>Comprehend basic data types such as vectors, matrices, and factors and their usage in data manipulation and analysis.</li><li>Master data frames, lists, relational operators, and logical operators and their usage in data manipulation and analysis.</li><li>Develop proficiency in conditional statements, loops, and functions and their usage in data manipulation and analysis.</li><li>Learn and use R packages, apply family functions, regular expressions, dates and times, and data manipulation using the Dplyr package.</li><li>Create and plot data visualisations in R.</li></ul><p><strong>R Programming for Data Science Level 3, 4 &amp; 5</strong><strong>: </strong></p><p>This course is designed to provide individuals with a comprehensive understanding of data science and R programming. The course includes three levels: Level 3, Level 4, and Level 5.</p><p>Level 3 Curriculum provides an overview of data science and introduces students to R programming language. This level consists of six units: Data Science Overview, R and RStudio, Introduction to Basics, Vectors, Matrices, and Factors.</p><p>Level 4 Curriculum focuses on data manipulation and analysis using R. This level consists of seven units: Data Frames, Lists, Relational Operators, Logical Operators, Conditional Statements, Loops, and Functions.</p><p>Level 5 Curriculum focuses on advanced topics in data manipulation and analysis using R.</p><p><strong>Certificate</strong></p><ul><li>In pursuing this QLS-endorsed course, learners attain a profound understanding and amplify their professional prowess, enriching their curriculum vitae.</li><li>The Quality Licence Scheme stands as a reputable and distinguished endorsement, emblematic of exceptional standards and excellence throughout the UK.</li><li>Upon completion, learners will receive a Diploma in R Programming for Data Science at QLS Level 5 Endorsed hard copy certificate, making it easy for them to showcase their achievements.</li></ul><p>Please Note: Imperial Academy is One Education approved resale partner for Quality Licence Scheme Endorsed courses.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        15 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This course is suitable for the following:</p><ul><li>Students and professionals who want to learn data science and R programming language.</li><li>Professionals who want to upskill themselves for better career opportunities.</li><li>Data analysts who want to enhance their data manipulation and analysis approaches using R.</li><li>Programmers who want to learn data science.</li><li>Entrepreneurs who want to learn data science to improve their business decisions.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Anyone can enrol in this R Programming for Data Science course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst - £22k-£51k per year</li><li>Data Scientist - £29k-£72k per year</li><li>Business Intelligence Analyst - £27k-£57k per year</li><li>Machine Learning Engineer - £36k-£98k per year</li><li>Data Engineer - £30k-£75k per year</li><li>Database Administrator - £25k-£60k per year</li><li>Statistician - £25k-£65k per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Imperial Academy

Level 5 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited

R Programming for Data Science Level 3, 4 & 5

<strong>Learn to design a resilient and scalable cloud solution using traffic load balancers, availability sets, and scale sets</strong>
        <p>Cloud computing is one of the biggest technology revolutions in the IT industry, spreading at the speed of light all over the world. A growing number of business companies and organizations are looking for ways to migrate their applications onto the cloud or build new cloud-native applications from scratch on top of cloud infrastructure. The demand for more skilled people in the arena of cloud computing is increasing every day across multiple industries. Companies are looking for cloud experts!

Becoming a Cloud Expert - If you are keen to become a cloud expert, then this course is designed to help you build on your knowledge of, and experience with, cloud computing while using the Azure cloud platform. The training program is divided into levels.

Level 3 is all about resilience and scalability. 

In level 1, we learned to build an end-to-end cloud solution on the Microsoft Azure platform; 
In level 2, we learned to monitor the performance and health of our cloud solution. 

Now it is time to learn how to design a system that will be more resilient to failures and better able to accommodate unpredictable capacity demands.

All the code files are placed at https://github.com/PacktPublishing/Becoming-a-Cloud-Expert---Microsoft-Azure-IaaS---Level-3</p>
        <h2>What You Will Learn</h2><p>Master the main functions, types, and rules of a Traffic Load Balancer<br>Deploy and manage Azure network and application LBs<br>How to design a more resilient cloud solution<br>Understand the fault and update domain concepts<br>Deploy and manage Azure availability sets<br>Perform performance optimization with capacity scaling<br>Use vertical and horizontal scaling<br>Deploy and manage Azure scale sets</p>
        <h2>Audience</h2><p>This course is for IT Professionals who are keen to gain a strong understanding of Cloud Computing and want to enhance their service and deployment models. Cloud administrators, IT and cloud experts, and DevOps engineers will also benefit from the course.</p>
        <h2>Approach</h2><p>This exhaustive course is packed with step-by-step instructions, practical examples, and helpful advice.</p>
        <h2>Key Features</h2><p>A comprehensive overview of the Microsoft Azure platform * Practice while building an end-to-end demo system * The third level in Becoming a Cloud Expert training program</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/becoming-a-cloud-expert---microsoft-azure-iaas---level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome!</p><p style='margin: 0'>Getting Started: Welcome!</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Objectives and Structure</p><p style='margin: 0'>Getting Started: Course Objectives and Structure</p></td></tr></tbody></table><br><h4>2. Traffic Load Balancing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Traffic Load Balancing: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. What is a Load Balancer?</p><p style='margin: 0'>Traffic Load Balancing: What is a Load Balancer?</p></td></tr><tr><td style='padding: 15px;'><p>3. Network and Application LBs</p><p style='margin: 0'>Traffic Load Balancing: Network and Application LBs</p></td></tr><tr><td style='padding: 15px;'><p>4. Public and Internal LBs</p><p style='margin: 0'>Traffic Load Balancing: Public and Internal LBs</p></td></tr><tr><td style='padding: 15px;'><p>5. Azure Load Balancer</p><p style='margin: 0'>Traffic Load Balancing: Azure Load Balancer</p></td></tr><tr><td style='padding: 15px;'><p>6. Demo - Setup a Web Servers Cluster</p><p style='margin: 0'>Traffic Load Balancing: Demo - Setup a Web Servers Cluster</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo - Azure Load Balancer</p><p style='margin: 0'>Traffic Load Balancing: Demo - Azure Load Balancer</p></td></tr></tbody></table><br><h4>3. Azure Availability Sets</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Azure Availability Sets: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Resiliency and Availability</p><p style='margin: 0'>Azure Availability Sets: Resiliency and Availability</p></td></tr><tr><td style='padding: 15px;'><p>3. Azure Redundancy Options</p><p style='margin: 0'>Azure Availability Sets: Azure Redundancy Options</p></td></tr><tr><td style='padding: 15px;'><p>4. Availability Set, Fault and Update Domains</p><p style='margin: 0'>Azure Availability Sets: Availability Set, Fault and Update Domains</p></td></tr><tr><td style='padding: 15px;'><p>5. Demo - Availability Sets</p><p style='margin: 0'>Azure Availability Sets: Demo - Availability Sets.</p></td></tr></tbody></table><br><h4>4. Azure VMs Scale Sets</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Azure VMs Scale Sets: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Performance Optimization and Capacity Scaling</p><p style='margin: 0'>Azure VMs Scale Sets: Performance Optimization and Capacity Scaling</p></td></tr><tr><td style='padding: 15px;'><p>3. Vertical and Horizontal Scaling</p><p style='margin: 0'>Azure VMs Scale Sets: Vertical and Horizontal Scaling</p></td></tr><tr><td style='padding: 15px;'><p>4. Automatic Scaling</p><p style='margin: 0'>Azure VMs Scale Sets: Automatic Scaling</p></td></tr><tr><td style='padding: 15px;'><p>5. What is an Azure Scale Set?</p><p style='margin: 0'>Azure VMs Scale Sets: What is an Azure Scale Set?</p></td></tr><tr><td style='padding: 15px;'><p>6. Demo - Web Server as Source VM Image</p><p style='margin: 0'>Azure VMs Scale Sets: Demo - Web Server as Source VM Image</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo - Create and Configure a Scale Set</p><p style='margin: 0'>Azure VMs Scale Sets: Demo - Create and Configure a Scale Set</p></td></tr><tr><td style='padding: 15px;'><p>8. Demo - Testing the Scale Set</p><p style='margin: 0'>Azure VMs Scale Sets: Demo - Testing the Scale Set</p></td></tr></tbody></table><br><h4>5. Course Summary</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Summary</p><p style='margin: 0'>Course Summary: Course Summary</p></td></tr></tbody></table>

Cloud Computing

Microsoft Azure

Learn to design a resilient and scalable cloud solution using traffic load balancers, availability sets, and scale sets

Becoming a Cloud Expert - Microsoft Azure IaaS - Level 3

<p><strong> 24-Hour Flash Sale! Prices Reduced Like Never Before!! </strong></p><p><strong> 7 in 1 Nursing Assistant &amp; End of Life Care Bundle Admission Gifts </strong></p><p><strong> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter </strong></p><p>Did you know that in the UK alone, the healthcare sector is projected to grow by <strong>2.8% in 2024</strong>, creating over <strong>2.1 million new jobs</strong>? With an ageing population and increasing demand for specialist services, the need for qualified nursing assistants is greater than ever. If you're drawn to a career in care, our <strong>Nursing Assistant Bundle</strong> could be your perfect first step.</p><p>This <strong>Nursing Assistant &amp; End of Life Care</strong> bundle equips you with the knowledge and skills to excel in various healthcare settings. From mastering the fundamentals of <strong>Patient Care</strong> to specialising in areas like <strong>End-of-life Care</strong>, <strong>Mental Health Nursing</strong>, and <strong>Palliative Care</strong>, you'll gain the confidence and expertise to provide compassionate and effective support to those in need.</p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong> Course 01: </strong>Advanced Diploma in Healthcare Assistant at QLS Level 5</li><li><strong> Course 02: </strong>Diploma in End of Life Care at QLS Level 4</li></ul><p><strong>CPD QS Accredited Courses:</strong></p><ul><li><strong> Course 03: </strong>Adult Nursing Training</li><li><strong> Course 04: </strong>Mental Health Nursing Diploma</li><li><strong> Course 05:</strong> Palliative Facilitator</li><li><strong> Course 06: </strong>Health and Social Care Management</li><li><strong> Course 07: </strong>Communication Skills in Health and Social Care</li></ul><p>This "Nursing Assistant &amp; End of Life Care" course is a step towards becoming more than just a healthcare professional; it's a journey to becoming a cornerstone of support and a bearer of light in the darkest times. This is your invitation to transform lives, including your own, by providing care that transcends the physical needs-care that touches the soul. Enrol in this Nursing Assistant bundle to make a fulfilling career in healthcare today!</p><p><strong>Learning Outcomes of this Bundle:</strong></p><ul><li>Understand the comprehensive roles and responsibilities of advanced healthcare assistants.</li><li>Gain in-depth insights into compassionate practices for end-of-life care.</li><li>Learn the fundamental skills and knowledge required in adult nursing care.</li><li>Acquire detailed knowledge and understanding of mental health nursing principles.</li><li>Develop specialised expertise in the field of palliative care facilitation.</li><li>Master the essential skills for effective management in health and social care.</li></ul><p><strong>Why Prefer this Course?</strong></p><ul><li>Get <strong>Free QLS Endorsed Certificates</strong> upon completion of the course</li><li>Get a <strong>Free Student ID Card</strong> with this training program (£10 postal charge will be applicable for international delivery)</li><li>The course is <strong>Affordable and Simple</strong> to understand</li><li>Get <strong>Lifetime Access</strong> to the course materials</li><li>The training program comes with <strong>24/7 Tutor Support</strong></li></ul> <p><strong>Start your learning journey straight away!</strong></p><p><strong> Course Curriculum of the QLS Endorsed Courses </strong></p><p><strong> Course 01: Advanced Diploma in Healthcare Assistant at QLS Level 5</strong></p><ul><li>Working in Different Healthcare Settings</li><li>Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care Part - 1</li><li>Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care Part - 2</li><li>Maintaining Medical Records</li><li>Confidentiality in a Medical Environment</li><li>Health and Safety Responsibilities</li><li>Hygiene in Nursing</li><li>Infection Control</li><li>Mobility and Immobility Issues of Patients in Nursing</li><li>Rights and Responsibilities as a Health and Social Care Worker</li><li>Role as A Caregiver and Healthcare Professional</li><li>Providing Care or Treatment to People Who Lack Capacity</li><li>Managing Service Delivery in Health and Social Care</li><li>Medical Jargon and Terminology</li><li>Effects of Covid-19 on Human Life</li><li>Preventions and Social Measures to Be Taken</li><li>Information Technology in Health Care</li><li>Artificial Intelligence, Data Science and Technological Solutions against Covid-19</li></ul><p><strong> Course 02: Diploma in End of Life Care at QLS Level 4</strong></p><ul><li>Module 01: Introduction to End of life Care</li><li>Module 02: Availability of Services</li><li>Module 03: Advance Care Planning</li><li>Module 04: Goals of Care</li><li>Module 05: Physical Illness</li><li>Module 06: Neurological Illness</li><li>Module 07: Quality Care at the End of Life</li><li>Module 08: Communication During the End of Life</li><li>Module 09: Palliative Care Holistic Assessment</li><li>Module 10: Spiritual and Psychosocial Care</li><li>Module 11: Care after Death</li><li>Module 12: Further Support, Training and Development</li><li>Module 13: Legal Aspects</li></ul><p><strong>=========&gt;&gt;&gt;&gt;&gt; And 5 More Courses &lt;&lt;&lt;&lt;&lt;=========</strong></p><p><strong>How will I get my Certificate?</strong></p><p>After successfully completing the course, you will be able to order your Certificates as proof of your achievement.</p><ul><li><strong>PDF Certificate:</strong> Free (Previously it was £12.99*7 = £91)</li><li><strong>CPD Hard Copy Certificate: </strong>Free (For The First 2 Courses: Previously it was £29.99*2 = 60)</li><li><strong>QLS Endorsed Hard Copy Certificate: </strong>Free (Previously it was £119 &amp; £99)</li></ul> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        310 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>Anyone interested in learning more about the topic is advised to take this bundle. This bundle is ideal for:</p><ul><li>Aspiring nurses</li><li>Healthcare assistants</li><li>Palliative care staff</li><li>Social workers</li><li>Career changers</li><li>Nursing students</li></ul><p><strong>Please note: </strong>This course doesn't entitle you to practice as a professional in this specific field. Rather, this course will assist you in understanding the fundamentals so that you can improve your knowledge in the relevant field.</p>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You will not need any prior background or expertise in this bundle.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>After completing this bundle, you are to start your career or begin the next phase of your career.</p><ul><li>Healthcare Assistant - £18,000 to £23,000</li><li>Palliative Care Nurse - £25,000 to £35,000</li><li>Mental Health Nurse - £24,000 to £30,000</li><li>Nursing Assistant - £17,000 to £22,000</li><li>Social Care Manager - £30,000 to £40,000</li><li>Palliative Care Coordinator - £23,000 to £28,000</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Digital Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p>Upon passing the Course, you need to order a <strong>Digital Certificate</strong> for each of the courses inside this bundle as proof of your new skills that are accredited by <strong>CPD QS </strong>for Free.</p></div>
        <h4>CPD Accredited Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>Hard copy certificate - Included<br /><br /></p><p><strong><em>Please note that International students have to pay an additional £10 as a shipment fee.</em></strong></p></div>
        <h4>QLS Endorsed Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><ul><li><strong>Advanced Diploma in Healthcare Assistant at QLS Level 5 - £119</strong></li><li><strong>Diploma in End-of-Life Care at QLS Level 4 - £99</strong></li></ul><p><em>Please note that International students have to pay an additional £10 as a shipment fee.</em></p></div>

                        </div>

Academy for Health and Fitness

Healthcare Assistant

Palliative Care Assistant

Healthcare Science Assistant

Emergency Care Assistant

End Of Life Care

Nursing Diploma

Nursing Assistant & End of Life Care - QLS Endorsed

<strong>Learn how to combine the three most important tools in data science: Python, SQL, and Tableau</strong>
        <p>Python, SQL, and Tableau are three of the most widely used tools in the world of data science. Python is the leading programming language
SQL is the most widely used means for communication with database systems
Tableau is the preferred solution for data visualization;

The course starts off by introducing software integration as a concept. We discuss some important terms such as servers, clients, requests, and responses. Moreover, you will learn about data connectivity, APIs, and endpoints. Then we continue by introducing the real-life example exercise the course is centred around: the Absenteeism at Work dataset. The preprocessing part that follows will give you a taste of what BI and data science look like in real-life, on-the-job situations. Then we continue by applying some Machine Learning to our data. You will learn how to explore the problem at hand from a machine-learning perspective, how to create targets, what kind of statistical preprocessing is necessary for this part of the exercise, how to train a Machine Learning model, and how to test it-a truly comprehensive ML exercise. Connecting Python and SQL is not immediate; we show how that's done in an entire section of the course.

By the end of that section, you will be able to transfer data from Jupyter to Workbench. And finally, as promised, Tableau will allow us to visualize the data we have been working with. We will prepare several insightful charts and will interpret the results together.

All the code files are placed at https://github.com/PacktPublishing/Python-SQL-Tableau-Integrating-Python-SQL-and-Tableau</p>
        <h2>What You Will Learn</h2><p>Create a module of the ML model for later use<br>Connect Python and SQL to transfer data from Jupyter to Workbench<br>Visualize data in Tableau<br>Analyze and interpret exercise outputs in Jupyter and Tableau</p>
        <h2>Audience</h2><p>This course is for anyone looking for a career in Business Intelligence and Data Science. Data scientists who are eagerly looking to break into the field and learn the necessary essentials of Data Science and software engineers who are interested in building intelligent applications driven by Python and Machine Learning will also benefit from this course.</p>
        <h2>Approach</h2><p>This course is designed so that each section covers a new scenario and uses a step-by-step approach to help you learn and understand each concept.</p>
        <h2>Key Features</h2><p>How to use Python, SQL, and Tableau together * Software integration * Data preprocessing techniques * Apply machine learning</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/python-sql-tableau-integrating-python-sql-and-tableau</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p>This section will give you a brief introduction about the course.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. (Free Preview): What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. What is software integration?</h4><p>This section will help you to understand about software integration?.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. (Free Preview): Properties and Definitions: Data, Servers, Clients, Requests and Responses</p><p style='margin: 0'>What is software integration?: Properties and Definitions: Data, Servers, Clients, Requests and Responses</p></td></tr><tr><td style='padding: 15px;'><p>2. (Free Preview): Properties and Definitions: Data Connectivity, APIs, and Endpoints</p><p style='margin: 0'>What is software integration?: Properties and Definitions: Data Connectivity, APIs, and Endpoints</p></td></tr><tr><td style='padding: 15px;'><p>3. Further Details on APIs</p><p style='margin: 0'>What is software integration?: Further Details on APIs</p></td></tr><tr><td style='padding: 15px;'><p>4. (Free Preview): Text Files as Means of Communication</p><p style='margin: 0'>What is software integration?: Text Files as Means of Communication</p></td></tr><tr><td style='padding: 15px;'><p>5. Definitions and Applications</p><p style='margin: 0'>What is software integration?: Definitions and Applications</p></td></tr></tbody></table><br><h4>3. Setting up the working environment</h4><p>This section will help you set up the working environment.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Setting Up the Environment - An Introduction (Do Not Skip, Please)!</p><p style='margin: 0'>Setting up the working environment: Setting Up the Environment - An Introduction (Do Not Skip, Please)!</p></td></tr><tr><td style='padding: 15px;'><p>2. Why Python and why Jupyter?</p><p style='margin: 0'>Setting up the working environment: Why Python and why Jupyter?</p></td></tr><tr><td style='padding: 15px;'><p>3. Installing Anaconda</p><p style='margin: 0'>Setting up the working environment: Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>4. The Jupyter Dashboard - Part 1</p><p style='margin: 0'>Setting up the working environment: The Jupyter Dashboard - Part 1</p></td></tr><tr><td style='padding: 15px;'><p>5. The Jupyter Dashboard - Part 2</p><p style='margin: 0'>Setting up the working environment: The Jupyter Dashboard - Part 2</p></td></tr><tr><td style='padding: 15px;'><p>6. Installing sklearn</p><p style='margin: 0'>Setting up the working environment: Installing sklearn</p></td></tr></tbody></table><br><h4>4. What's next in the course?</h4><p>This section will help you understand what this course is all about.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Up Ahead</p><p style='margin: 0'>What's next in the course?: Up Ahead</p></td></tr><tr><td style='padding: 15px;'><p>2. Real-Life Example: Absenteeism at Work</p><p style='margin: 0'>What's next in the course?: Real-Life Example: Absenteeism at Work</p></td></tr><tr><td style='padding: 15px;'><p>3. Real-Life Example: The Dataset</p><p style='margin: 0'>What's next in the course?: Real-Life Example: The Dataset</p></td></tr></tbody></table><br><h4>5. Preprocessing</h4><p>In this section, we'll learn about Preprocessing.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Sets in Python</p><p style='margin: 0'>Preprocessing: Data Sets in Python</p></td></tr><tr><td style='padding: 15px;'><p>2. Data at a Glance</p><p style='margin: 0'>Preprocessing: Data at a Glance</p></td></tr><tr><td style='padding: 15px;'><p>3. A Note on Our Usage of Terms with Multiple Meanings</p><p style='margin: 0'>Preprocessing: A Note on Our Usage of Terms with Multiple Meanings</p></td></tr><tr><td style='padding: 15px;'><p>4. Picking the Appropriate Approach for the Task at Hand</p><p style='margin: 0'>Preprocessing: Picking the Appropriate Approach for the Task at Hand</p></td></tr><tr><td style='padding: 15px;'><p>5. Removing Irrelevant Data</p><p style='margin: 0'>Preprocessing: Removing Irrelevant Data</p></td></tr><tr><td style='padding: 15px;'><p>6. Examining the Reasons for Absence</p><p style='margin: 0'>Preprocessing: Examining the Reasons for Absence</p></td></tr><tr><td style='padding: 15px;'><p>7. Splitting a Column into Multiple Dummies</p><p style='margin: 0'>Preprocessing: Splitting a Column into Multiple Dummies</p></td></tr><tr><td style='padding: 15px;'><p>8. Dummy Variables and Their Statistical Importance</p><p style='margin: 0'>Preprocessing: Dummy Variables and Their Statistical Importance</p></td></tr><tr><td style='padding: 15px;'><p>9. Grouping - Transforming Dummy Variables into Categorical Variables</p><p style='margin: 0'>Preprocessing: Grouping - Transforming Dummy Variables into Categorical Variables</p></td></tr><tr><td style='padding: 15px;'><p>10. Concatenating Columns in Python</p><p style='margin: 0'>Preprocessing: Concatenating Columns in Python</p></td></tr><tr><td style='padding: 15px;'><p>11. Changing Column Order in Pandas DataFrame</p><p style='margin: 0'>Preprocessing: Changing Column Order in Pandas DataFrame</p></td></tr><tr><td style='padding: 15px;'><p>12. Implementing Checkpoints in Coding</p><p style='margin: 0'>Preprocessing: Implementing Checkpoints in Coding</p></td></tr><tr><td style='padding: 15px;'><p>13. Exploring the Initial "Date" Column</p><p style='margin: 0'>Preprocessing: Exploring the Initial "Date" Column</p></td></tr><tr><td style='padding: 15px;'><p>14. Using the "Date" Column to Extract the Appropriate Month Value</p><p style='margin: 0'>Preprocessing: Using the "Date" Column to Extract the Appropriate Month Value</p></td></tr><tr><td style='padding: 15px;'><p>15. Introducing "Day of the Week"</p><p style='margin: 0'>Preprocessing: Introducing "Day of the Week"</p></td></tr><tr><td style='padding: 15px;'><p>16. Further Analysis of the DataFrame: Next 5 Columns</p><p style='margin: 0'>Preprocessing: Further Analysis of the DataFrame: Next 5 Columns</p></td></tr><tr><td style='padding: 15px;'><p>17. Further Analysis of the DaraFrame: "Education", "Children", "Pets"</p><p style='margin: 0'>Preprocessing: Further Analysis of the DaraFrame: "Education", "Children", "Pets"</p></td></tr><tr><td style='padding: 15px;'><p>18. A Final Note on Preprocessing</p><p style='margin: 0'>Preprocessing: A Final Note on Preprocessing</p></td></tr></tbody></table><br><h4>6. Machine Learning</h4><p>In this section, we'll learn about Machine Learning.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. (Free Preview): Exploring the Problem from a Machine Learning Point of View</p><p style='margin: 0'>Machine Learning: Exploring the Problem from a Machine Learning Point of View</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating the Targets for the Logistic Regression</p><p style='margin: 0'>Machine Learning: Creating the Targets for the Logistic Regression</p></td></tr><tr><td style='padding: 15px;'><p>3. Selecting the Inputs</p><p style='margin: 0'>Machine Learning: Selecting the Inputs</p></td></tr><tr><td style='padding: 15px;'><p>4. A Bit of Statistical Preprocessing</p><p style='margin: 0'>Machine Learning: A Bit of Statistical Preprocessing</p></td></tr><tr><td style='padding: 15px;'><p>5. Train-test Split of the Data</p><p style='margin: 0'>Machine Learning: Train-test Split of the Data</p></td></tr><tr><td style='padding: 15px;'><p>6. Training the Model and Assessing its Accuracy</p><p style='margin: 0'>Machine Learning: Training the Model and Assessing its Accuracy</p></td></tr><tr><td style='padding: 15px;'><p>7. Extracting the Intercept and Coefficients from a Logistic Regression</p><p style='margin: 0'>Machine Learning: Extracting the Intercept and Coefficients from a Logistic Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Interpreting the Logistic Regression Coefficients</p><p style='margin: 0'>Machine Learning: Interpreting the Logistic Regression Coefficients</p></td></tr><tr><td style='padding: 15px;'><p>9. Omitting the dummy variables from the Standardization</p><p style='margin: 0'>Machine Learning: Omitting the dummy variables from the Standardization</p></td></tr><tr><td style='padding: 15px;'><p>10. Interpreting the Important Predictors</p><p style='margin: 0'>Machine Learning: Interpreting the Important Predictors</p></td></tr><tr><td style='padding: 15px;'><p>11. Simplifying the Model (Backward Elimination)</p><p style='margin: 0'>Machine Learning: Simplifying the Model (Backward Elimination)</p></td></tr><tr><td style='padding: 15px;'><p>12. Testing the Machine Learning Model</p><p style='margin: 0'>Machine Learning: Testing the Machine Learning Model</p></td></tr><tr><td style='padding: 15px;'><p>13. How to Save the Machine Learning Model and Prepare it for Future Deployment</p><p style='margin: 0'>Machine Learning: How to Save the Machine Learning Model and Prepare it for Future Deployment</p></td></tr><tr><td style='padding: 15px;'><p>14. Creating a Module for Later Use of the Model</p><p style='margin: 0'>Machine Learning: Creating a Module for Later Use of the Model</p></td></tr></tbody></table><br><h4>7. Installing MySQL and Getting Acquainted with the Interface</h4><p>In this section, we'll Install MySQL and get Acquainted with the Interface.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Installing MySQL</p><p style='margin: 0'>Installing MySQL and Getting Acquainted with the Interface: Installing MySQL</p></td></tr><tr><td style='padding: 15px;'><p>2. Setting Up a Connection</p><p style='margin: 0'>Installing MySQL and Getting Acquainted with the Interface: Setting Up a Connection</p></td></tr><tr><td style='padding: 15px;'><p>3. Introduction to the MySQL Interface</p><p style='margin: 0'>Installing MySQL and Getting Acquainted with the Interface: Introduction to the MySQL Interface</p></td></tr></tbody></table><br><h4>8. Connecting Python and SQL</h4><p>This section will help you to connect Python and SQL.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Implementing the 'absenteeism_module' - Part I</p><p style='margin: 0'>Connecting Python and SQL: Implementing the 'absenteeism_module' - Part I</p></td></tr><tr><td style='padding: 15px;'><p>2. Implementing the 'absenteeism_module' - Part II</p><p style='margin: 0'>Connecting Python and SQL: Implementing the 'absenteeism_module' - Part II</p></td></tr><tr><td style='padding: 15px;'><p>3. Creating a Database in MySQL</p><p style='margin: 0'>Connecting Python and SQL: Creating a Database in MySQL</p></td></tr><tr><td style='padding: 15px;'><p>4. Importing and Installing 'pymysql'</p><p style='margin: 0'>Connecting Python and SQL: Importing and Installing 'pymysql'</p></td></tr><tr><td style='padding: 15px;'><p>5. Creating a Connection and Cursor</p><p style='margin: 0'>Connecting Python and SQL: Creating a Connection and Cursor</p></td></tr><tr><td style='padding: 15px;'><p>6. Creating the 'predicted_outputs' table in MySQL</p><p style='margin: 0'>Connecting Python and SQL: Creating the 'predicted_outputs' table in MySQL</p></td></tr><tr><td style='padding: 15px;'><p>7. Running an SQL SELECT Statement from Python</p><p style='margin: 0'>Connecting Python and SQL: Running an SQL SELECT Statement from Python</p></td></tr><tr><td style='padding: 15px;'><p>8. Transferring Data from Jupyter to Workbench - Part I</p><p style='margin: 0'>Connecting Python and SQL: Transferring Data from Jupyter to Workbench - Part I</p></td></tr><tr><td style='padding: 15px;'><p>9. Transferring Data from Jupyter to Workbench - Part II</p><p style='margin: 0'>Connecting Python and SQL: Transferring Data from Jupyter to Workbench - Part II</p></td></tr><tr><td style='padding: 15px;'><p>10. Transferring Data from Jupyter to Workbench - Part III</p><p style='margin: 0'>Connecting Python and SQL: Transferring Data from Jupyter to Workbench - Part III</p></td></tr></tbody></table><br><h4>9. Analyzing the Obtained data in Tableau</h4><p>In this section, we'll analyze the Obtained data.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Analysis in Tableau: Age vs Probability</p><p style='margin: 0'>Analyzing the Obtained data in Tableau: Analysis in Tableau: Age vs Probability</p></td></tr><tr><td style='padding: 15px;'><p>2. Analysis in Tableau: Reasons vs Probability</p><p style='margin: 0'>Analyzing the Obtained data in Tableau: Analysis in Tableau: Reasons vs Probability</p></td></tr><tr><td style='padding: 15px;'><p>3. Analysis in Tableau: Transportation Expense vs Probability</p><p style='margin: 0'>Analyzing the Obtained data in Tableau: Analysis in Tableau: Transportation Expense vs Probability</p></td></tr></tbody></table>

Data Science

Tableau

Python

Pandas

Learn how to combine the three most important tools in data science: Python, SQL, and Tableau

Python, SQL, and Tableau: Integrating Python, SQL, and Tableau

<strong>Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis</strong>
        <p>In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in Python, you can give your company a competitive edge and level up in your career. This course will give you a robust grounding in clustering and classification, the main aspects of machine learning.

The course consists of 7 sections that will help you master Python machine learning. You'll begin with an introduction to Python data science and Anaconda, which is a powerful Python-driven framework for data science. Next, you'll delve into Pandas and read data structures, including CSV, Excel, and HTML data. As you advance, you'll perform data cleaning and munging to remove NAs\no data and discover how to handle conditional data, group by attributes, and do much more. You'll also grasp basic concepts of unsupervised learning such as K-means clustering and its implementation on the Iris dataset. The course will take you through the theory of dimension reduction and feature selection for machine learning and help you understand Principal Component Analysis (PCA) using two case studies. You'll get to grips with the linear and non-linear classification of SVM along with Gradient Boosting Machine (GBM) and Naive Bayes Classification. Finally, you'll explore neural networks and discover the powerful H20 framework and for deep learning classification. Additionally, you'll learn about perceptrons and Artificial Neural Networks (ANN) for binary classification.
By the end of this course, you'll be able to use packages such as NumPy, Pandas, and Matplotlib to work with real data in Python.

All code and supporting files for this course are available at 
https://github.com/sanjanapackt/PacktPublishing-Clustering-and-Classification-with-Machine-Learning-in-Python.</p>
        <h2>What You Will Learn</h2><p>Harness the power of Anaconda/iPython for practical data science<br>Read data into the Python environment from different sources<br>Carry out basic data preprocessing and wrangling in Python<br>Implement unsupervised/clustering techniques such as k-means clustering<br>Get to grips with dimensionality reduction techniques and feature selection<br>Implement supervised learning/classification techniques such as random forests<br>Explore neural network- and deep learning-based classification<br></p>
        <h2>Audience</h2><p>If you want to build data science applications in the Python environment, this book is for you. You'll also find this book helpful if you want to learn how to implement unsupervised learning on real data using Python, or if you're a student looking to get started with artificial neural networks and deep learning.</p>
        <h2>Approach</h2><p>This course is full of interesting and illustrative examples and easy-to-understand theory to help you implement a real-world concept in every lecture. It covers hands-on methods to simplify and address even the most difficult concepts in Python using minimal jargon.</p>
        <h2>Key Features</h2><p>Explore the most important Python data science concepts and packages, including Pandas * Master the Anaconda framework and use it to implement clustering and classification models on your data * Get to grips with data science fundamentals and understand which models should be used when * </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/clustering-and-classification-with-machine-learning-in-r</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome to Clustering & Classification with Machine Learning in Python</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: Welcome to Clustering & Classification with Machine Learning in Python</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Machine Learning?</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: What is Machine Learning?</p></td></tr><tr><td style='padding: 15px;'><p>3. Python Data Science Environment</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: Python Data Science Environment</p></td></tr><tr><td style='padding: 15px;'><p>4. For Mac Users</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: For Mac Users</p></td></tr><tr><td style='padding: 15px;'><p>5. IPython in Browser</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: IPython in Browser</p></td></tr><tr><td style='padding: 15px;'><p>6. Python Data Science Packages to Be Used</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: Python Data Science Packages to Be Used</p></td></tr></tbody></table><br><h4>2. Read in Data from Different Sources with Pandas</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What are Pandas?</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: What are Pandas?</p></td></tr><tr><td style='padding: 15px;'><p>2. Read in Data from CSV</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Data from CSV</p></td></tr><tr><td style='padding: 15px;'><p>3. Read in Online CSV</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Online CSV</p></td></tr><tr><td style='padding: 15px;'><p>4. Read in Excel Data</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Excel Data</p></td></tr><tr><td style='padding: 15px;'><p>5. Read in HTML Data</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in HTML Data</p></td></tr><tr><td style='padding: 15px;'><p>6. Read in Data from Databases</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Data from Databases</p></td></tr></tbody></table><br><h4>3. Data Cleaning & Munging</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Remove Missing Values</p><p style='margin: 0'>Data Cleaning & Munging: Remove Missing Values</p></td></tr><tr><td style='padding: 15px;'><p>2. Conditional Data Selection</p><p style='margin: 0'>Data Cleaning & Munging: Conditional Data Selection</p></td></tr><tr><td style='padding: 15px;'><p>3. Data Grouping</p><p style='margin: 0'>Data Cleaning & Munging: Data Grouping</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Sub setting</p><p style='margin: 0'>Data Cleaning & Munging: Data sub setting</p></td></tr><tr><td style='padding: 15px;'><p>5. Ranking & Sorting</p><p style='margin: 0'>Data Cleaning & Munging: Ranking & Sorting</p></td></tr><tr><td style='padding: 15px;'><p>6. Concatenate</p><p style='margin: 0'>Data Cleaning & Munging: Concatenate</p></td></tr><tr><td style='padding: 15px;'><p>7. Merging & Joining Data Frames</p><p style='margin: 0'>Data Cleaning & Munging: Merging & Joining Data Frames</p></td></tr></tbody></table><br><h4>4. Unsupervised Learning in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Unsupervised Classification- Some Basic Concepts</p><p style='margin: 0'>Unsupervised Learning in Python: Unsupervised Classification- Some Basic Concepts</p></td></tr><tr><td style='padding: 15px;'><p>2. K-Means Clustering: Theory</p><p style='margin: 0'>Unsupervised Learning in Python: K-Means Clustering: Theory</p></td></tr><tr><td style='padding: 15px;'><p>3. Implement K-Means on the Iris Data</p><p style='margin: 0'>Unsupervised Learning in Python: Implement K-Means on the Iris Data</p></td></tr><tr><td style='padding: 15px;'><p>4. Quantifying K-Means Clustering Performance</p><p style='margin: 0'>Unsupervised Learning in Python: Quantifying K-Means Clustering Performance</p></td></tr><tr><td style='padding: 15px;'><p>5. K-Means Clustering with Real Data</p><p style='margin: 0'>Unsupervised Learning in Python: K-Means Clustering with Real Data</p></td></tr><tr><td style='padding: 15px;'><p>6. How to Select the Optimal Number of Clusters?</p><p style='margin: 0'>Unsupervised Learning in Python: How to Select the Optimal Number of Clusters?</p></td></tr><tr><td style='padding: 15px;'><p>7. Gaussian Mixture Modelling (GMM)</p><p style='margin: 0'>Unsupervised Learning in Python: Gaussian Mixture Modelling (GMM)</p></td></tr><tr><td style='padding: 15px;'><p>8. Hierarchical Clustering-theory</p><p style='margin: 0'>Unsupervised Learning in Python: Hierarchical Clustering-theory</p></td></tr><tr><td style='padding: 15px;'><p>9. Hierarchical Clustering-practical</p><p style='margin: 0'>Unsupervised Learning in Python: Hierarchical Clustering-practical</p></td></tr></tbody></table><br><h4>5. Dimension Reduction & Feature Selection for Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Principal Component Analysis (PCA)-Theory</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Principal Component Analysis (PCA)-Theory</p></td></tr><tr><td style='padding: 15px;'><p>2. Principal Component Analysis (PCA)-Case Study 1</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Principal Component Analysis (PCA)-Case Study 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Principal Component Analysis (PCA)-Case Study 2</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Principal Component Analysis (PCA)-Case Study 2</p></td></tr><tr><td style='padding: 15px;'><p>4. Linear Discriminant Analysis (LDA) for Dimension Reduction</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Linear Discriminant Analysis (LDA) for Dimension Reduction</p></td></tr><tr><td style='padding: 15px;'><p>5. t-SNE Dimension Reduction</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: t-SNE Dimension Reduction</p></td></tr><tr><td style='padding: 15px;'><p>6. Feature Selection to Select the Most Relevant Predictors</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Feature Selection to Select the Most Relevant Predictors</p></td></tr><tr><td style='padding: 15px;'><p>7. Recursive Feature Elimination (RFE)</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Recursive Feature Elimination (RFE)</p></td></tr></tbody></table><br><h4>6. Supervised Learning: Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Concepts Behind Supervised Learning</p><p style='margin: 0'>Supervised Learning: Classification: Concepts Behind Supervised Learning</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Preparation for Supervised Learning</p><p style='margin: 0'>Supervised Learning: Classification: Data Preparation for Supervised Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Pointers on Evaluating the Accuracy of Classification Modelling</p><p style='margin: 0'>Supervised Learning: Classification: Pointers on Evaluating the Accuracy of Classification Modelling</p></td></tr><tr><td style='padding: 15px;'><p>4. Using Logistic Regression as a Classification Model</p><p style='margin: 0'>Supervised Learning: Classification: Using Logistic Regression as a Classification Model</p></td></tr><tr><td style='padding: 15px;'><p>5. kNN- Classification</p><p style='margin: 0'>Supervised Learning: Classification: kNN- Classification</p></td></tr><tr><td style='padding: 15px;'><p>6. Naive Bayes Classification</p><p style='margin: 0'>Supervised Learning: Classification: Naive Bayes Classification</p></td></tr><tr><td style='padding: 15px;'><p>7. Linear Discriminant Analysis</p><p style='margin: 0'>Supervised Learning: Classification: Linear Discriminant Analysis</p></td></tr><tr><td style='padding: 15px;'><p>8. SVM- Linear Classification</p><p style='margin: 0'>Supervised Learning: Classification: SVM- Linear Classification</p></td></tr><tr><td style='padding: 15px;'><p>9. Non-Linear SVM Classification</p><p style='margin: 0'>Supervised Learning: Classification: Non-Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>10. RF-Classification</p><p style='margin: 0'>Supervised Learning: Classification: RF-Classification</p></td></tr><tr><td style='padding: 15px;'><p>11. Gradient Boosting Machine (GBM)</p><p style='margin: 0'>Supervised Learning: Classification: Gradient Boosting Machine (GBM)</p></td></tr><tr><td style='padding: 15px;'><p>12. Voting Classifier</p><p style='margin: 0'>Supervised Learning: Classification: Voting Classifier</p></td></tr></tbody></table><br><h4>7. Neural Networks and Deep Learning Based Classification Techniques</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Perceptrons for Binary Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Perceptrons for Binary Classification</p></td></tr><tr><td style='padding: 15px;'><p>2. Artificial Neural Networks (ANN) for Binary Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Artificial Neural Networks (ANN) for Binary Classification</p></td></tr><tr><td style='padding: 15px;'><p>3. Multi-class Classification With MLP</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Multi-class Classification With MLP</p></td></tr><tr><td style='padding: 15px;'><p>4. Introduction to H20</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Introduction to H20</p></td></tr><tr><td style='padding: 15px;'><p>5. Use H20 for Deep Learning Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Use H20 for Deep Learning Classification</p></td></tr><tr><td style='padding: 15px;'><p>6. H20 Deep Learning for Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: H20 Deep Learning for Classification</p></td></tr></tbody></table>

Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis

Clustering and Classification with Machine Learning in Python

<strong>Plan, deploy and monitor cloud solutions in Microsoft Azure's Infrastructure as a Service</strong>
        <p>Cloud computing is one of the biggest technology revolutions in the IT industry, spreading at the speed of light all over the world. A growing number of business companies and organizations are looking for ways to migrate their applications into the cloud or build new cloud-native applications from scratch on top of cloud infrastructure. The demand for skilled people in the area of cloud computing is increasing every day across industries. Companies are looking for cloud experts!

This course is part of a training program called Becoming a Cloud Expert. It sets a strong foundation for the viewers to learn to migrate applications onto the cloud while using the Infrastructure as a Service model inside Microsoft Azure.

Join us and start your journey as a cloud expert while learning to create a variety of virtual resources in Microsoft Azure. 
All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Becoming-a-Cloud-Expert---Microsoft-Azure-IaaS---Level-1</p>
        <h2>What You Will Learn</h2><p>Understand the building blocks of Azure s Infrastructure as a Service<br>Create virtual networks and subnets, and allocate private and public IP addresses<br>Plan and deploy Windows and Linux virtual machines<br>Configure traffic filtering using security rules<br>Manage and attach virtual disks to VMs<br>Create and manage storage accounts<br>Configure users and access role assignments<br>Operate and monitor ongoing VM tasks</p>
        <h2>Audience</h2><p>This course is for Cloud administrators, DevOps engineers, IT and cloud experts.<br></p>
        <h2>Approach</h2><p>This complete course, packed with step-by-step instructions, working examples, and helpful advice, is clearly divided into small chunks to help you understand each topic individually and learn at your own pace.</p>
        <h2>Key Features</h2><p>A comprehensive overview of the Microsoft Azure platform * A unique balance between theoretical topics and practical demonstrations * Practice with a dedicated project book</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/becoming-a-cloud-expert---microsoft-azure-iaas---level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome!</p><p style='margin: 0'>Welcome and thanks for joining! this training program "Becoming a Cloud Expert".</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Objectives and Structure</p><p style='margin: 0'>Before we start lets' quickly review the course main learning objectives and high-level structure.</p></td></tr></tbody></table><br><h4>2. Introduction to Microsoft Azure</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Here, we look into the topics covered in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Cloud Computing Definition</p><p style='margin: 0'>Cloud Computing is the transformation of computer hardware, software and networks into a utility service just like electric, water or gas services. Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources, that can be rapidly provisioned and released with minimal management effort or service provider interaction.</p></td></tr><tr><td style='padding: 15px;'><p>3. Microsoft Azure Cloud</p><p style='margin: 0'>Microsoft Azure provides the services to build, manage and deploy applications on a global scale while using multiple data centre's connected with a super-fast network. The all hardware and software orchestration of the cloud infrastructure is done using a specialized cloud operating system, called "Microsoft Azure" developed by Microsoft.</p></td></tr><tr><td style='padding: 15px;'><p>4. Global Footprint</p><p style='margin: 0'>The cloud services are supposed to be available on a global scale and this is one of the most impressive things about a public cloud provider, like Microsoft Azure, Amazon AWS, or Google cloud. Those players are building and running a huge global infrastructure and every year they announce on additional regions.</p></td></tr><tr><td style='padding: 15px;'><p>5. Demo - Azure Portal Overview</p><p style='margin: 0'>Let's review the Azure web portal at a high level.</p></td></tr><tr><td style='padding: 15px;'><p>6. Azure Resource Manager (ARM)</p><p style='margin: 0'>As part of the management layer of Azure, there is a central component that is called Azure Resource Manager, or in short ARM. The azure resource manager is used to allocate and deploy resources, organize resources into groups, control access to resources and much more.</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo - Azure ARM</p><p style='margin: 0'>Let's review the Azure ARM inside Microsoft Azure - resources, resource groups, resource providers, accounts and subscriptions and deployment templates.</p></td></tr><tr><td style='padding: 15px;'><p>8. Azure Role-Based Access Control (RBAC)</p><p style='margin: 0'>Access management for resources is a critical function for any group of users or an organization that is using the Azure cloud. As administrators, the Role-based access control (RBAC) will be used to manage who has access to Azure resources and what they can do with those resources.</p></td></tr><tr><td style='padding: 15px;'><p>9. Demo - Azure RBAC</p><p style='margin: 0'>Let's create new users and assign them access to specific entities.</p></td></tr></tbody></table><br><h4>3. Azure IaaS - Networking</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Here, we investigate the topics covered in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is a Virtual Network?</p><p style='margin: 0'>A virtual network is basically a private isolated connectivity layer being used to connect between a group of cloud resources.</p></td></tr><tr><td style='padding: 15px;'><p>3. Virtual Network Setting</p><p style='margin: 0'>In azure a virtual network is a managed entity that users are creating with a group of setting that is configured during the creation process or added later when the virtual network entity is already created.</p></td></tr><tr><td style='padding: 15px;'><p>4. Demo - Creating a Virtual Network (VNet)</p><p style='margin: 0'>Let's review the all process end-to-end for creating virtual network and subnets.</p></td></tr><tr><td style='padding: 15px;'><p>5. IP Address Types</p><p style='margin: 0'>As soon as we have a new virtual network, we can create VMs as resources inside that virtual network or inside a specific subnet. Each resource must be assigned with at least one private IP address from the virtual network address range we allocated to the virtual network. In addition, if we want a resource inside the virtual network to access the outside internet than we will need to use also a public IP address.</p></td></tr><tr><td style='padding: 15px;'><p>6. VM, NICs and IP Configuration</p><p style='margin: 0'>We have an IP address, that can be public or private created dynamically or with a static configuration. In Azure, we have a virtual machine, network interfaces and IP configuration profiles. Let's see how those building blocks are connected and what kind of combination we can create.</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo - Network Interfaces and IP Configuration</p><p style='margin: 0'>Let's review how to create network interfaces, attach them to VMs and create or adjust IP configuration.</p></td></tr><tr><td style='padding: 15px;'><p>8. Network Security Group (NSG)</p><p style='margin: 0'>All public cloud providers provide an ability to filter traffic and I am planning to present how it is being done in Microsoft Azure. In Microsoft Azure, we can filter network traffic to and from resources in a virtual network with an entity that is called a network security group.</p></td></tr><tr><td style='padding: 15px;'><p>9. Application Security Group (ASG)</p><p style='margin: 0'>Another useful option in Azure related to traffic filtering is using application security groups. The focus is moving to the function of the applications. We can group VMs based on their function.</p></td></tr><tr><td style='padding: 15px;'><p>10. Demo - Configuring NSG and ASG</p><p style='margin: 0'>Let's review how to configure NSG and ASG.</p></td></tr></tbody></table><br><h4>4. Azure IaaS - Storage</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Here, we investigate the topics covered in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. The Power of a Cloud Storage</p><p style='margin: 0'>Cloud storage is basically the option to store data on the Internet through a cloud computing provider who manages and operates data storage as a service. We talked about it in the introduction course about cloud computing, but this is the power of the cloud, it is a public infrastructure enabling companies to build applications that can scale on a global level without the limitation of a single private data center.</p></td></tr><tr><td style='padding: 15px;'><p>3. Types of Cloud Storage Services</p><p style='margin: 0'>These are three main storage types Block Storage, File Storage, and Object Storage.</p></td></tr><tr><td style='padding: 15px;'><p>4. Azure Storage Services</p><p style='margin: 0'>Storage services provided by public cloud providers are basically a mix of multiple options to fulfill the storage needs of a variety of applications.</p></td></tr><tr><td style='padding: 15px;'><p>5. Storage Accounts</p><p style='margin: 0'>Any type of storage service, like blobs, files, disks, tables, and queues that we would like to use must be allocated inside a logical entity called a storage account. A storage account entity can be created and managed by us or in some cases it will be created and managed by Azure.</p></td></tr><tr><td style='padding: 15px;'><p>6. Storage Replication Options</p><p style='margin: 0'>One of the most valuable features in a public cloud is high data durability. We know that data can be damaged because of many things like hardware failures, network or power outages, or maybe some massive site disasters. Systems availability is a critical factor and data is a critical component in almost all systems. Therefore, Microsoft Azure and other cloud providers are replicating our data and enable us to configure the best replication option for our application.</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo - Creating and Managing a Storage Account</p><p style='margin: 0'>Let's review how to create and manage a storage account.</p></td></tr><tr><td style='padding: 15px;'><p>8. VMs Disks</p><p style='margin: 0'>In addition to allocating computing power and setting networking connectivity, we also would like the capability to allocate storage capacity as virtual disks. These are three types of azure VM disks: - Operating System Disk (OS Disk), Temporary Disk and Data Disk/s.</p></td></tr><tr><td style='padding: 15px;'><p>9. Demo - Create, Attach and Detach Data Disks</p><p style='margin: 0'>Let's review how to create, attach and detach data disks for VMs.</p></td></tr><tr><td style='padding: 15px;'><p>10. Encryption Data at Rest</p><p style='margin: 0'>Security is a critical part of any solution, including the storage. Our next topic is encryption data at rest, meaning data that is stored in Microsoft Azure storage services. These are Azure Key Vault, SSE - Storage Service Encryption, Azure Disk Encryption (ADE).</p></td></tr></tbody></table><br><h4>5. Azure IaaS - Compute</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Here, we investigate the topics covered in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Virtualization</p><p style='margin: 0'>At the core technology value of virtualization, we can say that virtualization is a great way to divide and optimize physical IT resources into logical entities or also called virtual resources. It is an abstraction management layer of physical objects into logical objects. Now going back to the cloud, a public cloud environment is all about virtualization but of course in a much larger scale than a single private data center.</p></td></tr><tr><td style='padding: 15px;'><p>3. Virtual Machines</p><p style='margin: 0'>VM as a resource can be allocated on-demand while using the flexibility comes with a cloud environment. We can define the required VM from a variety of VM types and sizes to be used for different application workloads.</p></td></tr><tr><td style='padding: 15px;'><p>4. VM Types and Sizes</p><p style='margin: 0'>Every application or better call it workload will have a different resources consumption profile. Some applications require more CPUs power and less memory capacity and in other cases, it will be the other way around, more memory and less CPU power. So Microsoft Azure provides us with a variety of VM types and sizes that will be more optimized to the resources consumption profile of our application.</p></td></tr><tr><td style='padding: 15px;'><p>5. Demo - Creating a VMs</p><p style='margin: 0'>Let's review how to create a new VM while following the all process end-to-end.</p></td></tr><tr><td style='padding: 15px;'><p>6. Demo - VMs Settings</p><p style='margin: 0'>VMs Setting are as follows: - Attaching Network Interfaces, attaching Data Disks, scaling a VM Size, security recommendations, adding Extensions and configure access control.</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo - VMs Operations</p><p style='margin: 0'>VMs Operations are as follows: - shutdown/Run a VM, configure Auto-shutdown, scheduled Backups, using Inventory management for installed software components and running scripts on a VM.</p></td></tr><tr><td style='padding: 15px;'><p>8. Demo - VMs Monitoring</p><p style='margin: 0'>VMs Monitoring include - analysing activity logs, review metrics or KPIs, configure alerts, configure the diagnostics setting and review the Azure Advisor recommendations</p></td></tr></tbody></table><br><h4>6. Mission is Possible! - Your Project Assignment</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Mission Briefing</p><p style='margin: 0'>Do you choose to accept your mission?</p></td></tr></tbody></table><br><h4>7. Course Summary</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Let's Summarize</p><p style='margin: 0'>Let's recap the main topics we covered in this course.</p></td></tr><tr><td style='padding: 15px;'><p>2. What Next?</p><p style='margin: 0'>Thank you for joining and learning this course, let's speak about your next step moving forward as a Cloud Expert.</p></td></tr></tbody></table>

DevOps

Plan, deploy and monitor cloud solutions in Microsoft Azure's Infrastructure as a Service

Becoming a Cloud Expert - Microsoft Azure IaaS - Level 1

<p>Are you ready to take your data analysis skills to the next level? Introducing the Statistical Analysis and Data Science bundle - the ultimate collection of courses for anyone looking to dive deeper into the world of data.</p><p>The bundle features a QLS-endorsed course, which means you will receive a QLS hardcopy certificate upon completion. This certificate is a mark of quality and can help you stand out in a competitive job market. But that's not all - the bundle also includes 10 other relevant courses, all CPD-QS accredited, to ensure you have a comprehensive understanding of statistical analysis and data science.</p><p>You'll learn everything from the basics of statistical analysis to advanced SAS programming and big data analytics. Our courses were designed by people who are passionate about sharing their knowledge with you. With our easy-to-follow modules, you'll be able to learn at your own pace and from the comfort of your own home.</p><p>Whether you're a seasoned data analyst looking to expand your skills or a newcomer to the field, the Statistical Analysis and Data Science bundle has everything you need to succeed. So why wait? Enrol now and take the first step towards becoming a data analysis expert!</p><p><strong>This Diploma in Statistical Analysis at QLS Level 5</strong><strong> Bundle Package includes:</strong></p><ul><li><strong>Course 01:</strong> Diploma in Statistical Analysis at QLS Level 5</li></ul><p><strong>10 Premium Additional CPD QS Accredited Courses -</strong></p><ul><li><strong>Course 01: </strong>Data Analytics with Tableau</li><li><strong>Course 02: </strong>Big Data Analytics with PySpark Tableau Desktop and MongoDB</li><li><strong>Course 03: </strong>Data Science &amp; Machine Learning with R Training</li><li><strong>Course 04: </strong>SQL for Data Science, Data Analytics and Data Visualization</li><li><strong>Course 05: </strong>Advanced SAS Programming Using MacrosSQL</li><li><strong>Course 06: </strong>SQL NoSQL Big Data and Hadoop</li><li><strong>Course 07: </strong>Statistical Concepts and Application with R</li><li><strong>Course 08: </strong>Business Data Analysis</li><li><strong>Course 09: </strong>Business Intelligence and Data Mining Diploma</li><li><strong>Course 10: </strong>Data Analysis In Excel</li></ul><p><strong>Why Prefer This </strong><strong>Statistical Analysis and Data Science</strong><strong> Bundle?</strong></p><ul><li>You will receive a completely free certificate from the Quality Licence Scheme</li><li>Option to purchase 10 additional certificates accredited by CPD</li><li>Get a free Student ID Card - (£10 postal charges will be applicable for international delivery)</li><li>Free assessments and immediate success results</li><li>24/7 Tutor Support</li></ul> <p><strong>After taking this </strong><strong>Statistical Analysis and Data Science</strong><strong> bundle courses, you will be able to learn:</strong></p><ul><li>Develop a comprehensive understanding of statistical analysis and data science principles</li><li>Gain expertise in data analytics tools such as Tableau, PySpark, MongoDB, R, SQL, SAS, and Hadoop</li><li>Learn advanced data science techniques, including machine learning, data mining, and business intelligence</li><li>Acquire skills in data visualisation, data cleansing, and data analysis in Excel</li><li>Apply statistical concepts and methods to real-world scenarios</li><li>Build a strong foundation in data-driven decision-making</li><li>Develop problem-solving skills and learn how to make data-driven decisions</li></ul><p><strong>***Curriculum breakdown of Statistical Analysis</strong><strong>***</strong></p><ul><li>Module 01: The Realm of Statistics</li><li>Module 02: Basic Statistical Terms</li><li>Module 03: The Center of the Data</li><li>Module 04: Data Variability</li><li>Module 05: Binomial and Normal Distributions</li><li>Module 06: Introduction to Probability</li><li>Module 07: Estimates and Intervals</li><li>Module 08: Hypothesis Testing</li><li>Module 09: Regression Analysis</li><li>Module 10: Algorithms, Analytics and Predictions</li><li>Module 11: Learning From Experience: The Bayesian Way</li><li>Module 12: Doing Statistics: The Wrong Way</li><li>Module 13: How We Can Do Statistics Better</li></ul><p><strong>How is the </strong><strong>Statistical Analysis and Data Science</strong><strong>Bundle Assessment Process?</strong></p><p>You have to complete the assignment questions given at the end of the course and score a minimum of 60% to pass each exam. Our expert trainers will assess your assignment and give you feedback after you submit the assignment.</p><p>You will be entitled to claim a certificate endorsed by the Quality Licence Scheme after you successfully pass the exams.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        250 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Professionals looking to expand their skills in data analysis</li><li>Students interested in a career in data science and analytics</li><li>Entrepreneurs looking to make data-driven decisions</li><li>Anyone interested in learning statistical analysis and data science principles</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Our courses will prepare you for a range of careers, including:</p><ul><li>Data Analyst: £25,000 - £40,000</li><li>Business Analyst: £30,000 - £50,000</li><li>Data Scientist: £40,000 - £70,000</li><li>Business Intelligence Analyst: £35,000 - £55,000</li><li>Big Data Engineer: £50,000 - £80,000</li><li>Data Warehouse Architect: £60,000 - £100,000</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD QS Accredited Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p>Upon successfully completing the Bundle, you will need to place an order to receive a PDF Certificate for each course within the bundle. These certificates serve as proof of your newly acquired skills, accredited by CPD QS. Also, the certificates are recognised throughout the UK and internationally.</p></div>
        <h4>CPD QS Accredited Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>International students are subject to a £10 delivery fee for their orders, based on their location.</p></div>
        <h4>Diploma in Statistical Analysis at QLS Level 5</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Statistical Analysis and Data Science

<p>Are you interested in a career as a computer programmer, but not sure where to start? Look no further than our 20-in-1 Ultimate Computer Programmer bundle! This comprehensive package includes 25 premium courses designed to give you the skills and knowledge you need to succeed in this exciting field.</p><p>From IT support and network security to web design and database administration, our bundle covers all the essential topics for today's computer programmers. You'll also learn programming languages like Python, C#, and JavaScript, as well as tools like Kali Linux and Ruby on Rails.</p><p>But that's not all! We also include five career-guided courses to help you navigate the job market and advance your career. Learn how to write a winning CV, ace your next interview, and create a professional LinkedIn profile that will get you noticed by recruiters.</p><p>With our Computer Programmer bundle, you'll have everything you need to start your journey towards a successful career in programming. Don't wait - sign up today and start learning from the best!</p><p><strong>This 20-in-1 Ultimate Computer Programmer bundle consists of the following Courses:</strong></p><ul><li>Course 01: IT Support Technician</li><li>Course 02: Network Security and Risk Management</li><li>Course 03: CompTIA Network</li><li>Course 04: Information Management</li><li>Course 05: Web Application Penetration Testing Course</li><li>Course 06: Ruby on Rails Web Application Framework</li><li>Course 07: Learn Spring &amp; Angular Material with a Full Web Application</li><li>Course 08: JavaScript</li><li>Course 09: Learn Ethical Hacking with Kali Linux</li><li>Course 10: Python Course</li><li>Course 11: Web Design (HTML, CSS, Bootstrap) Complete Course</li><li>Course 12: Cisco ASA Firewall Training Course</li><li>Course 13: C# (C-Sharp)</li><li>Course 14: It: Ethical Hacking, IT Security and IT Cyber Attacking</li><li>Course 15: SQL Database Administrator</li><li>Course 16: MS Word Essentials - The Complete Word Course - Level 3</li><li>Course 17: Coding with Scratch</li><li>Course 18: Diploma in Front-End Web Development (HTML, CSS, &amp; Bootstrap)</li><li>Course 19: Data Science and Visualisation with Machine Learning</li><li>Course 20: PLC Programming Diploma</li></ul><p><strong>Additionally, you will get 5 other career-guided courses in this Computer Programmer bundle:</strong></p><ul><li>Course 01: Career Development Plan Fundamentals</li><li>Course 02: CV Writing and Job Searching</li><li>Course 03: Interview Skills: Ace the Interview</li><li>Course 04: Video Job Interview for Job Seekers</li><li>Course 05: Create a Professional LinkedIn Profile</li></ul><p><strong><em>So, stop scrolling down and procure the skills and aptitude with Apex Learning to outshine all your peers by enrolling in this </em></strong><strong>Computer Programmer<em> bundle</em>.</strong></p><p><strong>Learning Outcomes of Computer Programmer bundle </strong></p><p>This career-oriented bundle will help you to</p><ul><li>Gain the ability to excel in the role of Computer Programmer</li><li>Know your responsibilities as Computer Programmer and convey your experience to others</li><li>Gain excellent interpersonal and communication skills</li><li>Know the pros and cons of working as Computer Programmer</li><li>Adhere to the regulations around this area</li><li>Gain time and risk management skills to ensure efficiency</li><li>Manipulate technological advancement to become more effective</li><li>Maintain moral standards and set an example for your peers</li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        250 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>Anyone from any background can enrol in this Computer Programmer bundle.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Computer Programmer course has been designed to be fully compatible with tablets and smartphones.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Having this expertise will increase the value of your CV and open you up to multiple job sectors.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>You will get the Hard Copy certificate for the title course (<strong>IT Support Technician</strong>) absolutely Free! Other Hard Copy certificates are available for £10 each. </p><p><strong>Please Note:</strong> The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p> </div>

                        </div>

Apex Learning

Computer Programmer Training

<p>Are you interested in becoming a well-rounded IT professional? Our "IT: Programming, Web Development, Networking &amp; Security Complete Training" bundle is the perfect choice for you!</p><p>This bundle package consists of 30 premium courses, carefully curated to provide you with a comprehensive understanding of various IT domains. From web development to networking and security, this bundle has got you covered.</p><p>Get experience with essential IT skills, including Python, SQL, JavaScript, C#, and more. Our courses are designed to equip you with the knowledge and skills required to succeed in your IT career.</p><p>You will also learn about data protection, ethical hacking, and cybersecurity through our specialized courses. Our bundle includes a course on GDPR, a crucial compliance regulation, and ethical hacking with Kali Linux.</p><p>In addition to technical skills, we also offer career-focused courses, including career development plan fundamentals, CV writing, job searching, and interview skills. You will also learn how to create a professional LinkedIn profile to boost your job search.</p><p>Invest in your career and enroll in our "IT: Programming, Web Development, Networking &amp; Security Complete Training" bundle. With our expert-led courses and career-focused modules, you will gain a competitive edge in the IT industry. Start your journey today!</p><p><strong>This </strong><strong>All About IT </strong><strong>Bundle consists of the following premium courses: </strong></p><ul><li>Course 01: IT Support Technician</li><li>Course 02: Network Security and Risk Management</li><li>Course 03: CompTIA Network</li><li>Course 04: Information Management</li><li>Course 05: Web Application Penetration Testing Course</li><li>Course 06: CompTIA Cloud+ (CV0-002)</li><li>Course 07: PLC Programming Diploma</li><li>Course 08: Coding with Scratch</li><li>Course 09: Create Your First Responsive Website</li><li>Course 10: Python Course</li><li>Course 11: Diploma in Front-End Web Development (HTML, CSS, &amp; Bootstrap)</li><li>Course 12: SQL For Data Analytics &amp; Database Development</li><li>Course 13: C# (C-Sharp)</li><li>Course 14: Computer Operating System and Troubleshooting</li><li>Course 15: MS Word Essentials - The Complete Word Course - Level 3</li><li>Course 16: Building Your Own Computer Course</li><li>Course 17: ASP.Net MVC and Entity Framework Course</li><li>Course 18: It: Ethical Hacking, IT Security and IT Cyber Attacking</li><li>Course 19: Learn Ethical Hacking with Kali Linux</li><li>Course 20: SQL Database Administrator</li><li>Course 21: JavaScript</li><li>Course 22: Web Design (HTML, CSS, Bootstrap) Complete Course</li><li>Course 23: Data Science and Visualisation with Machine Learning</li><li>Course 24: Cisco ASA Firewall Training Course</li><li>Course 25: Data Protection (GDPR) Practitioner</li></ul><p><strong>Moreover, this bundles include 5 career-focused courses:</strong></p><ul><li>Course 01: Career Development Plan Fundamentals</li><li>Course 02: CV Writing and Job Searching</li><li>Course 03: Interview Skills: Ace the Interview</li><li>Course 04: Video Job Interview for Job Seekers</li><li>Course 05: Create a Professional LinkedIn Profile</li></ul><p>Our cutting-edge learning package offers top-notch digital aid and first-rate tutor support. You will acquire the crucial hard and soft skills needed for career advancement because this bundle has been thoroughly examined and is career-friendly. So don't overthink! Enrol today.</p><p><strong>Learning Outcomes</strong></p><p>This unique All About IT mega bundle will help you to-</p><ul><li>Quench your thirst for knowledge</li><li>Be up-to-date about the latest advancements</li><li>Achieve your dream career goal in this sector</li><li>Know the applicable rules and regulations needed for a professional in this area</li><li>Acquire some valuable knowledge related to All About IT to uplift your morale</li></ul><p>The bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your expertise and essential knowledge, which will assist you in reaching your goal. Moreover, you can learn from any place in your own time without travelling for classes.</p><p><strong>Certificate:</strong></p><ul><li><strong>PDF Certificate: Free for all </strong><strong>30</strong><strong> courses</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course: Previously it was £10)</strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        300 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>The All About IT bundle is designed to assist anyone with a curious mind, anyone looking to boost their CVs or individuals looking to upgrade their career to the next level can also benefit from the learning materials.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>The courses in this bundle has been designed to be fully compatible with tablets and smartphones.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This All About IT bundle will give you an edge over other competitors and will open the doors for you to a plethora of career opportunities.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>You will get the Hard Copy certificate for the title course (<strong>IT Support Technician</strong>) absolutely Free! Other Hard Copy certificates are available for £10 each. </p><p><strong>Please Note:</strong> The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p> </div>

                        </div>

IT: Programming, Web Development, Networking & Security Complete Training

<p dir="ltr" id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Python for Data Science and Machine Learning Bootcamp course is a comprehensive and practical online course designed to teach you how to use Python for data science and machine learning.&nbsp;</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">The course comprises several sections, each covering different topics in data science and machine learning. It includes all the essential concepts and tools required to analyse and manipulate data, build machine learning models, and make predictions or decisions.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">It also covers dataset summary statistics and visualisation techniques using libraries like matplotlib and seaborn. You&#39;ll learn about model selection, ensemble approaches, and parameter adjustment. Additionally, you will discover how to export and load machine learning models using libraries such as pickle and joblib.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">This course is suitable for anyone interested in learning Python for data science and machine learning. Whether you are an aspiring data scientist or a professional looking to enhance your data analysis skills, this course will help you achieve your goals.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Enrol now to gain instant access to the course materials and start your learning journey. Don&#39;t miss out on the opportunity to acquire one of the most in-demand skills in the market today and become a proficient data scientist and machine learning expert.</span></p><h2 dir="ltr" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Learning Outcome of Python for Data Science and Machine Learning Bootcamp &nbsp; &nbsp;</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Master fundamental concepts of machine learning and its applications.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Gain proficiency in Python programming for data analysis and manipulation.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Learn to load, read, and manipulate CSV data files using Python libraries.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Understand dataset summary statistics and visualization techniques.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Acquire skills in data preparation and preprocessing.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Explore feature selection techniques and their importance.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Evaluate machine learning algorithms using various techniques and metrics.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Spot check classification and regression algorithms for model selection.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Improve model performance using ensemble methods and parameter tuning.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Learn to export, save, load, and finalize machine learning models for real-time predictions.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Key Features of the Course</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">A CPD certificate that is recognised worldwide.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">A great online learning experience.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Interesting and unique online materials and activities.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Expert guidance and support from the field leaders.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Access to the study resources anytime you want.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Friendly and helpful customer service and admin support by email, phone, and chat from Monday through Friday.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Get a year-long access to the course.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Who is this course for</span></h2><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); font-size: 16px; box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0); font-size: 16px;">This Python for Data Science and Machine Learning Bootcamp Course is suitable for -</span></p><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Aspiring data scientists and machine learning enthusiasts</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Professionals seeking to enhance their data analysis skills</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Individuals interested in Python programming for data science</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Anyone looking to break into the field of data science and machine learning</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Students pursuing studies in computer science or related fields</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Requirements</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Basic understanding of programming concepts</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Familiarity with Python programming language is beneficial but not mandatory</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">No prerequisites; suitable for individuals from any academic background.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Accessible course materials from any internet-enabled device.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">CPD Certificate from Course Gate</span></h2><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for &pound;9 and the hard copy for &pound;15. Also, you can order both PDF and hardcopy certificates for &pound;22.</span></p><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Career path</span><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><br fr-original-style="" style="box-sizing: border-box;"></span></h2><div style="color: rgb(68, 68, 68); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(68, 68, 68);"><div style="color: rgb(68, 68, 68); box-sizing: border-box;" fr-original-style=" color: rgb(68, 68, 68);"><div data-elementor-type="wp-post" data-elementor-id="357815" data-elementor-post-type="course" fr-original-style="" style="box-sizing: border-box;"><section data-id="5767f71a" data-element_type="section" fr-original-style="" style="box-sizing: border-box;"><div data-id="58784751" data-element_type="column" fr-original-style="" style="box-sizing: border-box;"><div data-id="ce105db" data-element_type="widget" data-widget_type="text-editor.default" style="color: var( --e-global-color-text ); box-sizing: border-box;" fr-original-style=" color: var( --e-global-color-text );"><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Scientist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Machine Learning Engineer</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Business Intelligence Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Engineer</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Research Scientist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Quantitative Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Artificial Intelligence Specialist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Consultant</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Statistician</li></ul></div></div></section></div></div></div>

Course Gate

Machine Learning (ML)

Python for Data Science and Machine Learning Bootcamp online course is suitable for anyone interested in learning Python for data science and machine learning. It is especially ideal for aspiring data scientists and professionals seeking to enhance their data analysis skills. 

Python for Data Science and Machine Learning Bootcamp

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to become a data scientist, data engineer, data analyst or consultant</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know the different processes of Data Science, such as data cleaning, processing, wrangling, manipulation</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover the usage of R to clean, analyse, and visualise data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">How to write complex R programs for real-life industry situations</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get data scientist resume tips and land your first job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore the various applications of Machine Learning</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Understand Tidyverse, Operators, Vectors, Lists and their applications</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover data extraction and scraping</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">Data science and Machine Learning are one of the most popular fields in the world today. Data science is crucial to analysing and developing data-based machine learning models. We will cover programming in R in this course for data science and machine learning.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">R programming is a specialised skill that is in high demand today. We&rsquo;ve designed our&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Data Science and Machine Learning with R</b>&nbsp;course to help you learn fundamentals through advanced R programming. This course teaches you how to effectively use R programming for machine learning or data science. You&rsquo;ll learn how to visualise data by plotting in R, as well as how to clean data.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">You will receive training in machine learning and its many real-world applications while working with R Shiny to create web applications and online interactive dashboards. In other words, you will gain practical, step-by-step knowledge of the R programming language that you may use for business or professional purposes.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Data Science and Machine Learning with R&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To explore the world of R Programming Language, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Data Science +ML with R from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Getting Started with R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Types and Structures in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Intermediate R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Manipulation in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Visualization in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Creating Reports with R Markdown</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Building Webapps with R Shiny</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Preprocessing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear Regression: A Simple Model</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Exploratory Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear Regression: A Real Model</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Starting a Career in Data Science</strong></li></ul>

LearnDrive UK

The course covers data science and machine learning with R.



Data Science and Machine Learning with R

<strong>This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.</strong>
        <p>Machine learning is the key to development in many areas, such as IT, security, marketing, automation, and even medicine. Without machine learning, it is impossible to build intelligent applications and devices, such as Alexa, Siri, and Google Assistant. This course will help to get familiar with data science and machine learning.

The course starts with an introduction to data science, explaining different terms associated with it. You will also become familiar with machine learning and data science modeling and explore the key differences between model parameters and hyperparameters. Next, you will become familiar with the concepts of machine learning models, such as linear regression, decision trees, random forests, neural networks, and clustering techniques. Towards the end, you will learn how to evaluate machine learning models and learn the best practices to succeed in your data scientist role.

By the end of this course, you will have a solid understanding of data science and machine learning fundamentals.</p>
        <h2>What You Will Learn</h2><p>Become familiar with data science and machine learning terms<br>Distinguish between model parameters and hyperparameters<br>Distinguish between supervised and unsupervised learning<br>Discover how decision trees, bagging, and random forest works<br>Understand the importance of the k-nearest neighbors (KNN) algorithm in machine learning<br>Learn about neural networks and clustering techniques<br>Evaluate the performance of machine learning models</p>
        <h2>Audience</h2><p>This course is designed for students and beginners who want to understand the concepts, statistics, and math behind machine learning algorithms and for those who are curious to solve real-world problems using machine learning and data science. Everything is taught from scratch; hence, there are no prerequisites to get started with this course.</p>
        <h2>Approach</h2><p>With the help of simple explanation, avoiding confusing mathematical notation and jargon, this course covers the fundamentals of data science, machine learning, and data mining from scratch.</p>
        <h2>Key Features</h2><p>Learn the fundamentals of data science, machine learning, and data mining * Learn interesting techniques to evaluate a machine learning model * Discover the best practices to solve real-world problems using machine learning</p>
        
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Welcome and Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome</p><p style='margin: 0'>This video welcomes you to the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Data Science</p><p style='margin: 0'>This video provides an introduction to data science.</p></td></tr></tbody></table><br><h4>2. Preliminary Requirements to Understand Data Science and Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Understanding Data Science Terms</p><p style='margin: 0'>This video provides an overview of the terms used in data science.</p></td></tr><tr><td style='padding: 15px;'><p>2. Understanding Machine Learning</p><p style='margin: 0'>This video presents an overview of machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>3. Types of Learning</p><p style='margin: 0'>This video explains the different types of learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Understanding Data Science Modeling</p><p style='margin: 0'>This video focuses on data science modeling.</p></td></tr><tr><td style='padding: 15px;'><p>5. Model parameters versus Hyperparameters</p><p style='margin: 0'>This video explains the difference between model parameters and hyperparameters.</p></td></tr></tbody></table><br><h4>3. Machine Learning Models</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How Linear Regression Works</p><p style='margin: 0'>This video demonstrates how linear regression works.</p></td></tr><tr><td style='padding: 15px;'><p>2. How Decision Trees Work</p><p style='margin: 0'>This video demonstrates how decision trees work.</p></td></tr><tr><td style='padding: 15px;'><p>3. How Bagging and Random Forest Work</p><p style='margin: 0'>This video demonstrates the working of bagging and random forest.</p></td></tr><tr><td style='padding: 15px;'><p>4. How Support Vector Machine Works</p><p style='margin: 0'>This video demonstrates the working of support vector machines.</p></td></tr><tr><td style='padding: 15px;'><p>5. Neural Networks- Part 1</p><p style='margin: 0'>This video is the first part of a two-part video that explains neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>6. Neural Networks- Part 2</p><p style='margin: 0'>This is the second part of the two-part video that explains neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>7. How Logistic Regression Works</p><p style='margin: 0'>This video demonstrates the working of logistic regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. How k-nearest neighbour (KNN) Works</p><p style='margin: 0'>This video focuses on KNN and explains how it works.</p></td></tr><tr><td style='padding: 15px;'><p>9. Clustering Techniques</p><p style='margin: 0'>This video explains the different clustering techniques.</p></td></tr></tbody></table><br><h4>4. Model Performance</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Evaluating Models Performance</p><p style='margin: 0'>This video demonstrates how to evaluate the performance of the models.</p></td></tr></tbody></table><br><h4>5. Best Practices for a Data Scientist</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Best Practices for a Data Scientist</p><p style='margin: 0'>This video explains the best practices used by data scientists.</p></td></tr></tbody></table>

Algorithms

This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.

Mastering Data Science and Machine Learning Fundamentals

<p>Feeling Stuck in Your Career? The Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle is Your Skill-Building Solution. This exceptional collection of <strong>30 premium courses</strong> is designed to encourage growth and improve your career opportunities. Suited to meet different interests and goals, the  Python for Data Science and Machine Learning bundle provides an engaging learning experience, helping you learn skills across various disciplines.</p><p>With Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle, you'll have a personalised journey that aligns with your career goals and interests. This comprehensive package helps you confidently tackle new challenges, whether entering a new field or enhancing your existing knowledge. The  Python for Data Science and Machine Learning bundle is your gateway to expanding your career options, increasing job demand, and enhancing your skill set.</p><p>By enrolling in this bundle, you'll receive complimentary PDF certificates for all courses, adding value to your resume at no extra cost. Develop key skills and achieve important progress in your career and personal development. Start your journey today and experience the transformative impact of the Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! bundle on your job life and career growth!</p><p><strong>This Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle Comprises the Following CPD Accredited Courses:</strong></p><ul><li><strong>Course 01</strong>: Machine Learning Basics</li><li><strong>Course 02</strong>: Azure Machine Learning</li><li><strong>Course 03</strong>: Python Intermediate Training</li><li><strong>Course 04</strong>: Python Data Science</li><li><strong>Course 05</strong>: 2021 Data Science &amp; Machine Learning with R from A-Z</li><li><strong>Course 06</strong>: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 07</strong>: Learn Python, JavaScript, and Microsoft SQL for Data science</li><li><strong>Course 08</strong>: Spatial Data Visualisation and Machine Learning in Python</li><li><strong>Course 09</strong>: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 10</strong>: Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li><strong>Course 11</strong>: Deep Learning &amp; Neural Networks Python - Keras</li><li><strong>Course 12</strong>: The Complete Python 3 Course: Beginner to Advanced!</li><li><strong>Course 13</strong>: Building Big Data Pipelines with PySpark MongoDB and Bokeh</li><li><strong>Course 14</strong>: Clinical Data Management with SAS Programming</li><li><strong>Course 15</strong>: Certificate in Data Entry and Management</li><li><strong>Course 16</strong>: Quick Data Science Approach from Scratch</li><li><strong>Course 17</strong>: SQL for Data Science, Data Analytics and Data Visualization</li><li><strong>Course 18</strong>: Programming AutoCAD with SQL Server Database Using C#</li><li><strong>Course 19</strong>: Big Data Analytics with PySpark Power BI and MongoDB</li><li><strong>Course 20</strong>: Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau</li><li><strong>Course 21</strong>: Develop Big Data Pipelines with R, Sparklyr &amp; Power BI</li><li><strong>Course 22</strong>: Data Center Training Essentials: Power &amp; Electrical</li><li><strong>Course 23</strong>: Business Intelligence and Data Mining</li><li><strong>Course 24</strong>: Data Analysis In Excel</li><li><strong>Course 25</strong>: CV Writing and Job Searching</li><li><strong>Course 26</strong>: Learn to Level Up Your Leadership</li><li><strong>Course 27</strong>: Networking Skills for Personal Success</li><li><strong>Course 28</strong>: Ace Your Presentations: Public Speaking Masterclass</li><li><strong>Course 29</strong>: Learn to Make a Fresh Start in Your Life</li><li><strong>Course 30</strong>: Motivation - Motivating Yourself &amp; Others</li></ul><p><strong>What will make you stand out?</strong></p><p>Upon completion of this online Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle, you will gain the following:</p><ul><li>CPD QS Accredited Proficiency with this  Python for Data Science and Machine Learning  Bundle </li><li>After successfully completing the  Python for Data Science and Machine Learning bundle, you will receive a FREE PDF Certificate from REED as evidence of your newly acquired abilities.</li><li>Lifetime access to the whole collection of learning materials of this  Python for Data Science and Machine Learning       Bundle</li><li>The online test with immediate results </li><li>You can study and complete the  Python for Data Science and Machine Learning bundle at your own pace.</li><li>Study for the  Python for Data Science and Machine Learning bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p>The  Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! bundle is a premier learning resource, with each course module holding respected CPD accreditation, symbolising exceptional quality. The content is packed with knowledge and is regularly updated to ensure it remains relevant. This bundle offers not just education but a constantly improving learning experience designed to enrich both your personal and professional development.</p><p>Advance the future of learning with the   Python for Data Science and Machine Learning bundle, a comprehensive, complete collection of 30 courses. Each course in the  Python for Data Science and Machine Learning bundle has been handpicked by our experts to provide a broad range of learning opportunities. Together, these modules form an important and well-rounded learning experience.</p><p>Our mission is to deliver high-quality, accessible education for everyone. Whether you are starting your career, switching industries, or enhancing your professional skills, the   Python for Data Science and Machine Learning bundle offers the flexibility and convenience to learn at your own pace. Make the   Python for Data Science and Machine Learning package your trusted partner in your lifelong learning journey.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        300 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>The Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! is perfect for:</p><ul><li>Expand your knowledge and skillset for a fulfilling career with the  Python for Data Science and Machine Learning bundle.</li><li>Become a more valuable professional by earning CPD certification and mastering in-demand skills with the  Python for Data Science and Machine Learning bundle.</li><li>Discover your passion or explore new career options with the diverse learning opportunities in the  Python for Data Science and Machine Learning bundle.</li><li>Learn on your schedule, in the comfort of your home - the  Python for Data Science and Machine Learning bundle offers ultimate flexibility for busy individuals.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You are warmly invited to register for this bundle. Please be aware that no formal entry requirements or qualifications are necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Gain a wide range of skills across various fields, improve your problem-solving capabilities, and keep current with industry trends. Perfect for those aiming for career advancement, exploring a new professional direction, or pursuing personal growth. Begin your journey with the  Python for Data Science and Machine Learning bundle.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Certificates</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Compliance Central

Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses!

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to clean, process, wrangle, and manipulate data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know how to create a resume for a data science job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get a grasp of NumPy by learning how to use it for numerical data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover the secret of different Python libraries, such as Pandas, Matplotlib, SciPy and many more</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore machine learning concepts and algorithms</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover developing custom data solutions</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">You can create various applications for science, math, machine learning, and data science with Python. As a high-level, general-purpose programming language, Python is quite English-like and easy to learn. This&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Python for Data Science and Machine Learning</b>&nbsp;course will show you how to use Python for machine learning and data science.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">During the course, you will master every useful Python skill you need for data science and machine learning. If you find it challenging to write complex Python programs, we&rsquo;ll show you how to do so with our step-by-step instructions based on actual work contexts. You&rsquo;ll also learn how to run the regression, classification, and clustering and the specifics of supervised and unsupervised machine learning. Moreover, this course covers how statistics work for data science, hypothesis testing, and much more.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Python for Data Science and Machine Learning&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To become a master of Python, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><br fr-original-style="" style="box-sizing: border-box;"></p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Python for Data Science &amp; Machine Learning from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science &amp; Machine Learning Concepts</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python For Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Statistics for Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Probability and Hypothesis Testing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">NumPy Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Pandas Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Data Visualization</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Loading &amp; Exploration</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Cleaning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Feature Selecting and Engineering</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear and Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K Nearest Neighbours</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Decision Trees</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Ensemble Learning and Random Forests</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Support Vector Machines</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K-Means</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">PCA</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science Career</strong></li></ul>

This course aims to teach you how to use Python for machine learning and data science.



Python for Data Science and Machine Learning

<strong>In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.</strong>
        <p>The course covers practical issues in statistical computing that include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R programming to mastery.

We understand that theory is important to build a solid foundation, we also understand that theory alone isn't going to get the job done so that's why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you!

R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

By the end of the course, you'll be a professional data scientist with R and confidently apply for jobs and will feel good knowing that you have the skills and knowledge to back it up.

All resources are placed here: https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-</p>
        <h2>What You Will Learn</h2><p>Learn data cleaning, processing, wrangling, and manipulation<br>Learn plotting in R (graphs, charts, plots, histograms, and more)<br>How to create a resume and land your first job as a data scientist<br>Learn machine learning and its various practical applications<br>Learn data and file management in R<br>Use R to clean, analyze, and visualize data</p>
        <h2>Audience</h2><p>This course is designed for beginners who want to learn about data science and machine learning. No prior knowledge of R is required.</p>
        <h2>Approach</h2><p>This course takes you through data analysis in R, data visualization in R, and machine learning with hands-on step-by-step training.</p>
        <h2>Key Features</h2><p>Learn data extraction and web scraping * Learn to build custom data solutions * Learn automating dynamic report generation</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-</p>
        <h2>About the Author</h2><h3>Juan E. Galvan</h3><p>Juan E. Galvan has been an entrepreneur since grade school. His background is in the tech space from digital marketing, e-commerce, web development to programming. He believes in continuous education with the best of a university degree without all the downsides of burdensome costs and inefficient methods. He looks forward to helping people expand their skillsets.</p>
        <h2>Course Outline</h2><h4>1. Data Science and Machine Leaning Course Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Science and Machine Learning Introduction Section Overview</p><p style='margin: 0'>This video explains data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Data Science?</p><p style='margin: 0'>This video explains the concept of data science.</p></td></tr><tr><td style='padding: 15px;'><p>3. Machine Learning Overview</p><p style='margin: 0'>This video gives an overview of machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Science + Machine Learning Marketplace</p><p style='margin: 0'>This video explains the various trends and job opportunities in data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>5. Who is this Course For?</p><p style='margin: 0'>This video explains the audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Data Science and Machine Learning Job Opportunities</p><p style='margin: 0'>This video explains the various job opportunities in data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Science Job Roles</p><p style='margin: 0'>This video explains the various job roles for data science.</p></td></tr></tbody></table><br><h4>2. Getting Started with R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Getting Started with R</p><p style='margin: 0'>This video explains the concept of R.</p></td></tr><tr><td style='padding: 15px;'><p>2. R Basics</p><p style='margin: 0'>This video explains the R basics.</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Files</p><p style='margin: 0'>This video explains working with files in R.</p></td></tr><tr><td style='padding: 15px;'><p>4. R Studio</p><p style='margin: 0'>This video explains R studio.</p></td></tr><tr><td style='padding: 15px;'><p>5. Tidyverse Overview</p><p style='margin: 0'>This video provides an overview of tidyverse.</p></td></tr><tr><td style='padding: 15px;'><p>6. Additional Resources</p><p style='margin: 0'>This video shares some additional resources that will help you get along with the course.</p></td></tr></tbody></table><br><h4>3. Data Types and Structures in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Types and Structures in R Section Overview</p><p style='margin: 0'>This video explains data types and structures in R.</p></td></tr><tr><td style='padding: 15px;'><p>2. Basic Types</p><p style='margin: 0'>This video explains basic types in R.</p></td></tr><tr><td style='padding: 15px;'><p>3. Vectors - Part One</p><p style='margin: 0'>This video introduces you to vectors.</p></td></tr><tr><td style='padding: 15px;'><p>4. Vectors - Part Two</p><p style='margin: 0'>This video explains how to interact with R using vectors.</p></td></tr><tr><td style='padding: 15px;'><p>5. Vectors: Missing Values</p><p style='margin: 0'>This video explains vector missing values.</p></td></tr><tr><td style='padding: 15px;'><p>6. Vectors: Coercion</p><p style='margin: 0'>This video explains coercion.</p></td></tr><tr><td style='padding: 15px;'><p>7. Vectors: Naming</p><p style='margin: 0'>This video explains vector naming.</p></td></tr><tr><td style='padding: 15px;'><p>8. Vectors: Miscellaneous</p><p style='margin: 0'>This video explains miscellaneous yet important concepts in vectors.</p></td></tr><tr><td style='padding: 15px;'><p>9. Working with Matrices</p><p style='margin: 0'>This video explains working with matrices.</p></td></tr><tr><td style='padding: 15px;'><p>10. Working with Lists</p><p style='margin: 0'>This video explains working with lists.</p></td></tr><tr><td style='padding: 15px;'><p>11. Introduction to Data Frames</p><p style='margin: 0'>This video explains introduction to data frames.</p></td></tr><tr><td style='padding: 15px;'><p>12. Creating Data Frames</p><p style='margin: 0'>This video explains creating data frames.</p></td></tr><tr><td style='padding: 15px;'><p>13. Data Frames: Helper Functions</p><p style='margin: 0'>This video explains data frames helper functions.</p></td></tr><tr><td style='padding: 15px;'><p>14. Data Frames: Tibbles</p><p style='margin: 0'>This video explains Tibbles in data frames.</p></td></tr></tbody></table><br><h4>4. Intermediate R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Intermedia R Section Introduction</p><p style='margin: 0'>This video introduces you to R section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Relational Operators</p><p style='margin: 0'>This video explains relational operators.</p></td></tr><tr><td style='padding: 15px;'><p>3. Logical Operators</p><p style='margin: 0'>This video explains logical operators,</p></td></tr><tr><td style='padding: 15px;'><p>4. Conditional Statements</p><p style='margin: 0'>This video explains conditional statements.</p></td></tr><tr><td style='padding: 15px;'><p>5. Working with Loops</p><p style='margin: 0'>This video explains working with loops.</p></td></tr><tr><td style='padding: 15px;'><p>6. Working with Functions</p><p style='margin: 0'>This video explains working with functions in R.</p></td></tr><tr><td style='padding: 15px;'><p>7. Working with Packages</p><p style='margin: 0'>This video explains working with packages.</p></td></tr><tr><td style='padding: 15px;'><p>8. Working with Factors</p><p style='margin: 0'>This video explains working with factors in R.</p></td></tr><tr><td style='padding: 15px;'><p>9. Dates and Times</p><p style='margin: 0'>This video explains dates and times in R.</p></td></tr><tr><td style='padding: 15px;'><p>10. Functional Programming</p><p style='margin: 0'>This video explains functional programming.</p></td></tr><tr><td style='padding: 15px;'><p>11. Data Import/Export</p><p style='margin: 0'>This video explains how to import and export data in R.</p></td></tr><tr><td style='padding: 15px;'><p>12. Working with Databases</p><p style='margin: 0'>This video explains working with databases.</p></td></tr></tbody></table><br><h4>5. Data Manipulation in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Manipulation Section Introduction</p><p style='margin: 0'>This video introduces you to data manipulation.</p></td></tr><tr><td style='padding: 15px;'><p>2. Tidy Data</p><p style='margin: 0'>This video explains tidy data.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Pipe Operator</p><p style='margin: 0'>This video explains pipe operator.</p></td></tr><tr><td style='padding: 15px;'><p>4. {dplyr}: The Filter Verb</p><p style='margin: 0'>This video explains the filter verb.</p></td></tr><tr><td style='padding: 15px;'><p>5. {dplyr}: The Select Verb</p><p style='margin: 0'>This video explains the select verb</p></td></tr><tr><td style='padding: 15px;'><p>6. {dplyr}: The Mutate Verb</p><p style='margin: 0'>This video explains the mutate verb.</p></td></tr><tr><td style='padding: 15px;'><p>7. {dplyr}: The Arrange Verb</p><p style='margin: 0'>This video explains the purpose of arrange verb.</p></td></tr><tr><td style='padding: 15px;'><p>8. {dplyr}: The Summarize Verb</p><p style='margin: 0'>This video explains the summarize verb.</p></td></tr><tr><td style='padding: 15px;'><p>9. Data Pivoting: {tidyr}</p><p style='margin: 0'>This video explains data pivoting.</p></td></tr><tr><td style='padding: 15px;'><p>10. String Manipulation: {stringr}</p><p style='margin: 0'>This video explains string manipulation.</p></td></tr><tr><td style='padding: 15px;'><p>11. Web Scraping: {rvest}</p><p style='margin: 0'>This video explains web scraping.</p></td></tr><tr><td style='padding: 15px;'><p>12. JSON Parsing: {jsonlite}</p><p style='margin: 0'>This video explains JSON parsing.</p></td></tr></tbody></table><br><h4>6. Data Visualization in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Visualization in R Section Introduction</p><p style='margin: 0'>This video explains data visualization in R.</p></td></tr><tr><td style='padding: 15px;'><p>2. Getting Started with Data Visualization in R</p><p style='margin: 0'>This video explains getting started with data visualization in R.</p></td></tr><tr><td style='padding: 15px;'><p>3. Aesthetics Mappings</p><p style='margin: 0'>This vide explains aesthetics mappings.</p></td></tr><tr><td style='padding: 15px;'><p>4. Single Variable Plots</p><p style='margin: 0'>This video focuses on single variable plots.</p></td></tr><tr><td style='padding: 15px;'><p>5. Two Variable Plots</p><p style='margin: 0'>This video explains two variable plots.</p></td></tr><tr><td style='padding: 15px;'><p>6. Facets, Layering, and Coordinate Systems</p><p style='margin: 0'>This video explains facets, layering, and coordinate systems.</p></td></tr><tr><td style='padding: 15px;'><p>7. Styling and Saving</p><p style='margin: 0'>This video explains styling and saving.</p></td></tr></tbody></table><br><h4>7. Creating Reports with R Markdown</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to R Markdown</p><p style='margin: 0'>This video is an introduction to R.</p></td></tr></tbody></table><br><h4>8. Building Webapps with R Shiny</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to R Shiny</p><p style='margin: 0'>This video is an introduction to R Shiny</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating a Basic R Shiny App</p><p style='margin: 0'>This video helps you to create a basic R shiny app.</p></td></tr><tr><td style='padding: 15px;'><p>3. Other Examples with R Shiny</p><p style='margin: 0'>This video gives various examples with R shiny.</p></td></tr></tbody></table><br><h4>9. Introduction to Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Machine Learning Part One</p><p style='margin: 0'>This video is an introduction to machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Machine Learning Part Two</p><p style='margin: 0'>This video explains the different approaches in machine learning.</p></td></tr></tbody></table><br><h4>10. Data Preprocessing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Preprocessing Introduction</p><p style='margin: 0'>This video introduces you to data preprocessing.</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Preprocessing</p><p style='margin: 0'>This video explains some of the practical ways of data preprocessing.</p></td></tr></tbody></table><br><h4>11. Linear Regression: A Simple Model</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression: A Simple Model Introduction</p><p style='margin: 0'>This video explains a simple linear regression model.</p></td></tr><tr><td style='padding: 15px;'><p>2. A Simple Model</p><p style='margin: 0'>This video demonstrates a practical application of machine learning.</p></td></tr></tbody></table><br><h4>12. Exploratory Data Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Exploratory Data Analysis Introduction</p><p style='margin: 0'>This video gives an introduction to exploratory data analysis.</p></td></tr><tr><td style='padding: 15px;'><p>2. Hands-on Exploratory Data Analysis</p><p style='margin: 0'>This video explains hands-on exploratory data analysis.</p></td></tr></tbody></table><br><h4>13. Linear Regression - a Real Model</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression - Real Model Section Introduction</p><p style='margin: 0'>This video explains linear regression - a real model.</p></td></tr><tr><td style='padding: 15px;'><p>2. Linear Regression in R - Real Model</p><p style='margin: 0'>This video explains linear regression in R.</p></td></tr></tbody></table><br><h4>14. Logistic Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Logistic Regression</p><p style='margin: 0'>This video introduces you to logistic regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression in R</p><p style='margin: 0'>This video explains logistic regression in R.</p></td></tr></tbody></table><br><h4>15. Starting a Career in Data Science</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Starting a Data Science Career Section Overview</p><p style='margin: 0'>This video explains section overview of data science and career.</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating a Data Science Resume</p><p style='margin: 0'>This video explains creating a data science resume.</p></td></tr><tr><td style='padding: 15px;'><p>3. Getting Started with Freelancing</p><p style='margin: 0'>This vide explains how to get started with freelancing.</p></td></tr><tr><td style='padding: 15px;'><p>4. Top Freelance Websites</p><p style='margin: 0'>This video focuses on top freelancing websites.</p></td></tr><tr><td style='padding: 15px;'><p>5. Personal Branding</p><p style='margin: 0'>This video explains personal branding.</p></td></tr><tr><td style='padding: 15px;'><p>6. Networking Do's and Don'ts</p><p style='margin: 0'>This video explains networking do's and don'ts.</p></td></tr><tr><td style='padding: 15px;'><p>7. Setting Up a Website</p><p style='margin: 0'>This video explains how to set up a website.</p></td></tr></tbody></table>

In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

Data Science and Machine Learning with R from A-Z Course [Updated for 2021]

Janets

Trainers Course

Data Science and Machine Learning using Python : A Bootcamp

Get involved in a learning adventure, mastering R from foundational basics to advanced techniques. This course is a gateway to the realm of data science. Explore statistical machine learning models and intricacies of deep learning and create interactive Shiny apps. Unleash the power of R and elevate your proficiency in data-driven decision-making.

R Ultimate 2023 - R for Data Science and Machine Learning

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					<div><p>Python has steadily ascended the ranks in the tech world, becoming the go-to language for Data Science and Machine Learning pursuits. Its versatility and ease of use make it an optimal choice for those aspiring to delve into the intricate realms of data analysis and algorithm development. By integrating Python into the curriculum, this course ensures that learners are not only familiar with the language but also proficient in its application to Data Science and Machine Learning tasks.</p><p>Data Science, on the other hand, stands as the backbone of today's analytical advancements. With the ever-growing quantum of data, there's a dire need for professionals who can sift through this data deluge and extract meaningful insights. This course simplifies complex topics, making it easier for learners to grasp the intricate mechanics of data manipulation, processing, and analysis. By emphasizing Python's role in Data Science, the course underscores its significance in modern-day data operations.</p><p>Lastly, Machine Learning represents the pinnacle of automated data analysis. With the power of Python and the methodologies of Data Science, Machine Learning enables systems to learn from data and make decisions. This course elucidates these concepts, teaching learners to design, implement, and test Machine Learning algorithms. The synergy of Python, Data Science, and Machine Learning in this curriculum ensures that, by the end, learners are well-equipped to tackle any data challenge that comes their way.</p></div>
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										<span>Develop real-time prediction models using trained datasets.</span>
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			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2> <b>Why should I take this course?</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Stay ahead in the rapidly growing field of Data Science and Machine Learning.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Enhance career opportunities in the tech and data sectors.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Learn to use Python, a leading programming language in Data Science.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Address real-world business challenges using data analysis.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Achieve proficiency in handling and visualizing large datasets.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2> <b>Career Path</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Data Scientist</span>
									</li>
								<li>
											<span>
													</span>
										<span>Machine Learning Engineer</span>
									</li>
								<li>
											<span>
													</span>
										<span>Python Data Analyst</span>
									</li>
								<li>
											<span>
													</span>
										<span>Predictive Modeler</span>
									</li>
								<li>
											<span>
													</span>
										<span>Data Visualization Expert</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2> <b>Requirements</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Basic understanding of programming concepts.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Eagerness to learn Data Science and Machine Learning.</span>
									</li>
								<li>
											<span>
													</span>
										<span>A computer with internet access.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>

Skill Up

Master Data Science and Machine Learning with Python to transform data-driven decisions in businesses.

Data Science & Machine Learning with Python

<strong>In the fast-paced world of data science and machine learning, you have to stay up-to-date and keep ahead of the competition. For this, you have to constantly be on the lookout for the latest trends in tools and techniques for data science and machine learning. You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career!</strong>
        <p>The objective of this course is to give you a strong foundation needed to excel in Julia and learn the core of the language as well as the applied side in the shortest amount of time possible.

We won't waste time with the theory of why Julia is fast. We will jump right into the details and start coding. You will quickly realize how easy it is to learn this state-of-the-art and promising language. You will see how you can start using Julia to excel in your current job without moving the whole stack to Julia immediately.

After explaining the basic concepts, we jump to case studies in data science and then machine learning. We apply both traditional machine learning models and then get to deep learning. You will see how Julia can help you create deep learning models from scratch in just a few lines of code and then move on to the state-of-the-art models without spending too much time.

This way, you get to learn the most important concepts in this subject in the shortest amount of time possible without having to deal with the details of the less relevant topics. Once you have developed an intuition of the important stuff, you can then learn the latest and greatest models even on your own!

By the end of the course, you will have a strong understanding of Julia programming language fundamentals.
The code files are available here: https://github.com/PacktPublishing/Julia-Programming-Language---From-Zero-to-Expert-</p>
        <h2>What You Will Learn</h2><p>Learn coding in Julia programming language<br>Use DataFrames (equivalent to Pandas) in Julia<br>Create ML models from scratch in a way that helps you make modifications easily<br>Learn data wrangling with Julia<br>Use Julia to perform data manipulation, Apache Arrow, grouping, and analysis<br>Classify using decision trees and random forests</p>
        <h2>Audience</h2><p>This course is for all levels of data science and machine learning practitioners aiming to enhance their abilities and skill level in DS and ML. Developers who want to know how to harness the power of big data can also go for this course.<br><br>A basic understanding of programming is a must. Understanding Python, basic data science (reading CSVs and so on), and basic concepts of deep learning (such as classification) is not necessary but would be helpful.</p>
        <h2>Approach</h2><p>This course follows a code-oriented and case-study-based approach. In this course, you will learn by practice as every concept explanation is followed by practical implementation.</p>
        <h2>Key Features</h2><p>Learn the syntax of Julia and its differences from Python * Learn machine learning models, both traditional and deep * Explore data science case studies, including analysis and clustering</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Julia-Programming-Language---From-Zero-to-Expert-</p>
        <h2>About the Author</h2><h3>Dr. Mohammad Nauman</h3><p>Dr. Mohammad Nauman has a PhD in computer science and a PostDoc from the Max Planck Institute for software systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He holds extensive research experience with many state-of-the-art models. His research in Android security has led to some major shifts in the Android permission model.
He loves teaching and the most important reason he teaches online is to make sure that maximum people can learn through his content. Hope you have fun learning with him!</p>
        <h2>Course Outline</h2><h4>1. Intro and Setting up</h4><p></p><br><h4>2. Core Language Basics</h4><p></p><br><h4>3. Arrays and Matrices: Native Language Support</h4><p></p><br><h4>4. Functions and Fun Stuff</h4><p></p><br><h4>5. Getting Started with Data Science</h4><p></p><br><h4>6. Case Studies in Data Science</h4><p></p><br><h4>7. Deep Learning - Flux in Julia</h4><p></p><br><h4>8. Parting Words</h4><p></p>

Programming

In the fast-paced world of data science and machine learning, you have to stay up-to-date and keep ahead of the competition. For this, you have to constantly be on the lookout for the latest trends in tools and techniques for data science and machine learning. You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career!

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