Name: Level 3 Diploma in ICT Information Systems and Principles for IT Professionals
Price: 279 GBP
Availability: InStock

<p><strong>** Designed by Industry Specialist | FREE 2 CPD Courses With Certificates | QLS Level 7 Diploma Course | QLS Hard Copy Certificate Included | 180 CPD Points | Lifetime Access | Installment Payment | Tutor Support **</strong></p><p>In an era where 2.5 quintillion bytes of data are produced daily, the ability to process and visualise that data is paramount. Enter Power BI a tool that's taking the business world by storm. As the global market becomes increasingly data-driven, the role of a competent Data Analyst cannot be overstated. Introducing the "<strong>Data Analyst Training: Microsoft Power BI at QLS Level 7 Diploma</strong>." Designed by an industry specialist, this all-around course will guide you from the basics to the complicated nuances of <strong>Power BI</strong>, ensuring that you're not just a participant but a commander in the data revolution.</p><p>Experience beyond simple data processing. Get into 14 meticulously crafted modules that cover everything: from establishing a solid foundation in <strong>Module 01</strong> to storytelling with data in <strong>Module 08</strong> and even integrating advanced languages like Python in Module 07. With each module endorsed by <strong>Quality Licence Scheme</strong>, explore mini-projects that bolster your application abilities. By the end, you'll be adept at transforming raw data into actionable insights and compelling stories giving you a distinctive edge in the global job market.</p><p>As businesses globally recognise the vast potential of data, the demand for adept <strong>Power BI </strong>professionals skyrockets. But why be just another analyst when you can be the best? Train yourself with the "<strong>Data Analyst Training: Microsoft Power BI at QLS Level 7 Diploma</strong>" and harness the power of data to shape tomorrow. Your journey to global recognition begins here. Enrol now!</p><p><strong>â </strong><strong>ADDITIONAL GIFT </strong><strong>â</strong></p><ul><li><strong>Free Course 01:</strong> Google Data Studio</li><li><strong>Free Course 02:</strong> Business Intelligence and Data Mining</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Be competent in the foundational concepts of Microsoft Power BI, from installation to interface navigation.</li><li>Develop proficiency in data transformation techniques using the Query Editor.</li><li>Acquire advanced data transformation skills, including dynamic data operations with the M formula language.</li><li>Comprehend the intricacies of creating robust and efficient data models with diverse relationships.</li><li>Formulate compelling visual representations of data, exploring visuals like pie charts, treemaps, and matrix visuals.</li><li>Integrate Python within Power BI, enabling advanced data manipulation and machine learning capabilities.</li><li>Narrate compelling data-driven stories, leveraging effective visualisation and storytelling techniques.</li><li>Grasp the essentials of DAX for powerful data operations, from basic aggregations to context manipulations with CALCULATE.</li><li>Navigate and optimise the Power BI Cloud Service, ensuring seamless data presentations and platform sharing.</li><li>Implement advanced security measures, understanding row-level security for safeguarding sensitive data.</li></ul><p>Experience the transformative world of data with our "<strong>Data Analyst Training: Microsoft Power BI at QLS Level 7 Diploma</strong>". Prepared by industry experts, this course bridges foundational concepts with advanced techniques, enabling learners to extract, visualise, and communicate insights effectively. From conquering Power BI's interface to advanced data storytelling, integrate Python and DAX for a holistic analytical experience. Ideal for aspiring data professionals, this course promises hands-on projects and mastery of the global market.</p><p><strong>Course Curriculum:</strong></p><p><strong>** Data Analyst Training: Microsoft Power BI at QLS Level 7 Diploma **</strong></p><p><strong>Module 01: Introduction</strong></p><ul><li>Welcome!</li><li>What is Power BI?</li><li>Download &amp; Installing Power BI Desktop</li><li>Getting to know the interface</li><li>Mini Project: Transform Data</li><li>Mini Project: Visualise Data</li><li>Mini Project: Creating a Data Model</li><li>Course Outline: What will you learn in this course?</li><li>How to learn best with this course?</li></ul><p><strong>Module 02: Preparing our Project</strong></p><ul><li>Creating our initial project file</li><li>Working with the attached project files</li></ul><p><strong>Module 03: Data Transformation - The Query Editor</strong></p><ul><li>Exploring the Query Editor</li><li>Connecting to our data source</li><li>Editing rows</li><li>Changing data types</li><li>Replacing values</li><li>Close &amp; Apply</li><li>Connecting to a CSV file</li><li>Connecting to a web page</li><li>Extracting characters</li><li>Splitting &amp; merging columns</li><li>Creating conditional columns</li><li>Creating columns from examples</li><li>Merging Queries</li><li>Pivoting &amp; Unpivoting</li><li>Appending Queries</li><li>Practice &amp; Solution: Population table</li><li>The Fact-Dimension-Model</li><li>Practice: Load the dimension table</li><li>Organising our queries in groups</li><li>Entering data manually</li><li>Creating an index column</li><li>Workflow &amp; more transformations</li><li>Module summary</li><li>Exercise 1 - Instruction</li><li>Exercise Solution</li></ul><p><strong>Module 04: Data Transformation - Advanced</strong></p><ul><li>Advanced Editor - Best practices</li><li>Performance: References vs. Duplicating</li><li>Performance: Enable / Disable Load &amp; Report Refresh</li><li>Group by</li><li>Mathematical Operations</li><li>Run R Script</li><li>Using Parameters to transform data dynamically</li><li>M formula language: Basics</li><li>M formula language: Values, Lists &amp; Tables</li><li>M formula language: Functions</li><li>M formula language: More functions &amp; steps</li><li>Exercise 2 - Instructions</li><li>Exercise 2 - Solution</li></ul><p><strong>Module 05: Creating a Data Model</strong></p><ul><li>Understanding the relationship</li><li>Create &amp; edit relationships</li><li>One-to-many &amp; one-to-one relationship</li><li>Many-to-many (m:n) relationship</li><li>Cross filter direction</li><li>Activate &amp; deactivate relationships</li><li>Model summary</li><li>Exercise 3 Create Model</li><li>Exercise 3 Solution</li></ul><p><strong>Module 06: Data Visualisation</strong></p><ul><li>Our first visual</li><li>The format tab</li><li>Understanding tables</li><li>Conditional formatting</li><li>The Pie Chart</li><li>All about the filtered visual</li><li>The filter pane for developers</li><li>Cross-filtering &amp; editing interactions</li><li>Syncing slicers across pages</li><li>Creating drill-downs</li><li>Creating drill throughs</li><li>The treemap visual</li><li>The decomposition tree</li><li>Understanding the matrix visual</li><li>Editing pages</li><li>Buttons &amp; Actions</li><li>Bookmarks to customise your report</li><li>Analytics and Forecasts with line charts</li><li>Working with custom visuals</li><li>Get data using R Script &amp; R Script visual</li><li>Asking questions - Q&amp;A visual</li><li>Wrap up - data visualisation</li></ul><p><strong>Module 07: Power BI &amp; Python</strong></p><ul><li>Python in Power BI - Plan of attack</li><li>Setting up Python for Power BI</li><li>Transforming data using Python</li><li>Creating visualisations using Python</li><li>Violin plots, pair plots &amp; ridge plots using Python</li><li>Machine learning (Bayes Text Analyser) using Python</li><li>Performance &amp; Troubleshooting</li></ul><p><strong>Module 08: Storytelling with Data</strong></p><ul><li>Introduction</li><li>Show Empathy &amp; Identify the Requirement</li><li>Finding the Most Suitable KPIs</li><li>Choose an Effective Visualisation</li><li>Make Use of Natural Reading Patterns</li><li>Tell a Story Using Visual Cues</li><li>Avoid Chaos &amp; Group Information</li><li>Warp Up - Storytelling with Data</li></ul><p><strong>Module 09: DAX - The Essentials</strong></p><ul><li>Introduction</li><li>The project data</li><li>Measures vs. Calculated Columns</li><li>Automatically creating a date table in DAX</li><li>CALENDAR</li><li>Creating a complete date table with features</li><li>Creating a key measurement table</li><li>Aggregation functions</li><li>The different versions of COUNT</li><li>SUMX - Row-based calculations</li></ul><p><strong>Module 10: DAX - The CALCULATE function</strong></p><ul><li>Calculate - The basics</li><li>Changing the context with FILTER</li><li>All</li><li>All SELECTED</li><li>All Except</li></ul><p><strong>Module 11: Power BI Service - Power BI Cloud</strong></p><ul><li>How to go on now?</li><li>Power BI Pro vs Premium &amp; Signing up</li><li>Exploring the interface</li><li>Discovering your workspace</li><li>Connecting Power BI Desktop &amp; Cloud</li><li>Understanding datasets &amp; reports</li><li>Working on reports</li><li>Updating reports from Power BI Desktop</li><li>Creating and working with workspaces</li><li>Installing &amp; using a data gateway</li><li>Get Quick Insights</li><li>Creating dashboards</li><li>Sharing our results through Apps</li><li>Power BI Mobile App</li><li>Creating the layout for the Mobile App</li><li>Wrap up - Power BI Cloud</li></ul><p><strong>Module 12: Row-Level Security</strong></p><ul><li>Introduction</li><li>Creating a Row-Level Security</li><li>Row-Level Security in the Cloud</li><li>Row-Level Security &amp; Data Model</li><li>Dynamic Row-Level Security</li><li>Dynamic Many-to-Many RLS</li><li>Hierarchical Row-Level Security</li></ul><p><strong>Module 13: More data sources</strong></p><ul><li>JSON &amp; REST API</li><li>Setting up a local MySQL database</li><li>Connecting to a MySQL database in Power BI</li><li>Connecting to a SQL database (PostgreSQL)</li></ul><p><strong>Module 14: Next steps to improve &amp; stay up to date</strong></p><ul><li>Congratulations &amp; next steps</li><li>The End</li></ul><p><strong>Please Note:</strong> Imperial Academy is an approved reselling partner for Quality Licence Scheme courses under One Education.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        180 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 <strong>Data Analyst Training: Microsoft Power BI at QLS Level 7 Diploma </strong>course is appropriate for the following:</p><ul><li>Aspiring data analysts eager to kickstart or advance their careers in the data visualization realm.</li><li>Business professionals wanting to harness data-driven insights for strategic decision-making.</li><li>Researchers and academicians aiming to represent complex data sets in easily digestible visuals.</li><li>IT specialists looking to expand their skill set into the booming field of data analytics and visualization.</li><li>Companies and teams aiming to upskill their members for improved data-driven operations and reporting.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No requirements to enrol in <strong>Data Analyst Training: Microsoft Power BI at QLS Level 7 Diploma</strong>.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £25,000 - £40,000</li><li>Software Engineer: £28,000 - £55,000</li><li>Marketing Manager: £30,000 - £50,000</li><li>Civil Engineer: £27,000 - £52,000</li><li>Graphic Designer: £21,000 - £37,000</li><li>Solicitor: £30,000 - £60,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Hard Copy QLS Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>PDF Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Imperial Academy

Bring Your Data To Life | Designed by Industry Specialist |  Level 7 | QLS Endorsed Diploma | Certificate Included

Data Analyst Training: Microsoft Power BI at QLS Level 7 Diploma

General Admission

<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><strong>24 Hour Flash Deal **25-in-1 </strong><strong>Professional Functional Skills Maths Teacher </strong><strong>Mega Bundle**</strong></p><p><strong> </strong><strong>Professional Functional Skills Maths Teacher </strong><strong>Enrolment Gifts  **FREE PDF Certificate**FREE PDF Transcript ** FREE Exam** FREE Student ID ** Lifetime Access **FREE Enrolment Letter **</strong></p><p>Take the initial steps toward a successful long-term career by studying the <strong>Professional Functional Skills Maths Teacher</strong> package online with <strong>Studyhub </strong>through our online learning platform. The Professional Functional Skills Maths Teacher bundle can help you improve your CV, wow potential employers, and differentiate yourself from the mass.</p><p>This <strong>Professional Functional Skills Maths Teacher </strong>course provides complete 360-degree training on Professional Functional Skills Maths Teacher. You'll get not one, not two, not three, but <strong>twenty-five </strong>Professional Functional Skills Maths Teacher courses included in this course. Plus Studyhub's signature <strong>Forever Access</strong> is given as always, meaning these Professional Functional Skills Maths Teacher courses are yours for as long as you want them once you enrol in this course</p><p><strong>This Professional Functional Skills Maths Teacher Bundle consists the following career oriented courses:</strong></p><ul><li>Course 01: Functional Skills Maths</li><li>Course 02: Functional Skills Maths &amp; English Diploma</li><li>Course 03: Trigonometry Basics</li><li>Course 04: Applied Statistics</li><li>Course 05: Diploma in Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li>Course 06: Mathematics</li><li>Course 07: High School Math (Pure Mathematics 1)</li><li>Course 08: Excel in Personal Finance</li><li>Course 09: Creative Problem Solving</li><li>Course 10: Computer Simulation of Realistic Mathematical Models Training</li><li>Course 11: Calculus Level 1 - Learn Differentiation</li><li>Course 12: Computer Science with Python Course</li><li>Course 13: Adult Learning: A Comprehensive Guide for Educators</li><li>Course 14: Enhancing Adult Literacy and Numeracy Skills</li><li>Course 15: Adult Education Mastery Training</li><li>Course 16: Super Learning</li><li>Course 17: Teaching Techniques &amp; Methodologies Diploma</li><li>Course 18: Educational Psychology and Development</li><li>Course 19: Education Management course</li><li>Course 20: Planning, Delivery and Assessment in Teaching</li><li>Course 21: Teach to Engage Students</li><li>Course 22: Blended Learning Course for Teachers</li><li>Course 23: Understanding Specific Learning Difficulties: Supporting Diverse Learners</li><li>Course 24: Lesson Planning for Teaching</li><li>Course 25: Motivational Mentorship: Inspiring Personal and Professional Growth</li></ul> <p>The <strong>Professional Functional Skills Maths Teacher </strong>course has been prepared by focusing largely on Professional Functional Skills Maths Teacher career readiness. It has been designed by our Professional Functional Skills Maths Teacher specialists in a manner that you will be likely to find yourself head and shoulders above the others. For better learning, one to one assistance will also be provided if it's required by any learners.</p><p>The Professional Functional Skills Maths Teacher Bundle is one of the most prestigious training offered at StudyHub and is highly valued by employers for good reason. This Professional Functional Skills Maths Teacher bundle course has been created with <strong>twenty-five </strong>premium courses to provide our learners with the best learning experience possible to increase their understanding of their chosen field.</p><p>This Professional Functional Skills Maths Teacher Course, like every one of Study Hub's courses, is meticulously developed and well researched. Every one of the topics is divided into Professional Functional Skills Maths Teacher Elementary modules, allowing our students to grasp each lesson quickly.</p><p>The Professional Functional Skills Maths Teacher course is self-paced and can be taken from the comfort of your home, office, or on the go! With our Student ID card you will get discounts on things like music, food, travel and clothes etc.</p><p><strong>In this exclusive </strong><strong>Professional Functional Skills Maths Teacher </strong><strong>bundle, you really hit the jackpot. Here's what you get:</strong></p><ul><li>Step by step Professional Functional Skills Maths Teacher lessons</li><li>One to one assistance from Professional Functional Skills Maths Teacherprofessionals if you need it</li><li>Innovative exams to test your knowledge after the Professional Functional Skills Maths Teachercourse</li><li>24/7 customer support should you encounter any hiccups</li><li>Top-class learning portal</li><li>Unlimited lifetime access to all twenty-five Professional Functional Skills Maths Teacher courses</li><li>Digital Certificate, Transcript and student ID are all included in the price</li><li>PDF certificate immediately after passing</li><li>Original copies of your Professional Functional Skills Maths Teacher certificate and transcript on the next working day</li><li>Easily learn the Professional Functional Skills Maths Teacher skills and knowledge you want from the comfort of your home</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>This <strong>Professional Functional Skills Maths Teacher </strong>training is suitable for -</p><ul><li>Students</li><li>Recent graduates</li><li>Job Seekers</li><li>Individuals who are already employed in the relevant sectors and wish to enhance their knowledge and expertise in Professional Functional Skills Maths Teacher</li></ul><p>This course is suitable for anyone interested to further progress there career in:</p><ul><li>IGCSE Maths</li><li>A-Level Maths</li><li>Functional Skills Qualification in Maths at Level 2</li><li>Functional Skills Qualification in Mathematics at Level 2 - GCSE Equivalent for Maths</li><li>A-level Mathematics</li><li>Functional Skills Qualification in Mathematics at Entry 1</li><li>Functional Skills Qualification in Mathematics at Level 1</li><li>Functional Skills Qualification in English at Entry 1</li><li>Functional Skills Qualification in English at Entry 2</li><li>Functional Skills Qualification in English at Level 1</li><li>Functional Skills Qualification in ICT at Level 2</li><li>Functional Skills Qualification in Information &amp; Communication Technology (ICT) at Level 1</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>To participate in this <strong>Professional Functional Skills Maths Teacher </strong>course, all you need is -</p><ul><li>A smart device</li><li>A secure internet connection</li><li>And a keen interest in Professional Functional Skills Maths Teacher</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>You will be able to kickstart your <strong>Professional Functional Skills Maths Teacher </strong>career because this course includes various courses as a bonus. This Professional Functional Skills Maths Teacher is an excellent opportunity for you to learn multiple skills from the convenience of your own home and explore Professional Functional Skills Maths Teacher career opportunities.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><ul><li>CPD Accredited e-Certificate - Free</li><li>CPD Accredited Hardcopy Certificate - Free</li><li>Enrolment Letter - Free</li><li>Student ID Card - Free</li></ul></div>

                        </div>

Studyhub UK

Certificate in Further Education & Training (Cert Ed)

Higher Maths

Maths Teacher

Professional Skills Course (PSC)

Trainers Course

Diploma in Professional Studies (DPS)

Professional Functional Skills Maths Teacher - Advanced Diploma

<p><strong>Are you interested in pursuing a career in the </strong><strong>Computer Science Advanced Diploma</strong><strong> industry or learning more about it?</strong></p><p>If yes, then you have come to the right place. Our comprehensive courses on Computer Science Advanced Diploma will assist you in producing the best possible outcome by learning the Computer Science Advanced Diploma skills.</p><p>The Computer Science Advanced Diploma bundle is for those who want to be successful. In the Computer Science Advanced Diploma bundle, you will learn the essential knowledge needed to become well versed in Computer Science Advanced Diploma.</p><p><strong>Why would you choose the Computer Science Advanced Diploma course from Compliance Central:</strong></p><ul><li>Lifetime access to Computer Science Advanced Diploma courses materials</li><li>Full tutor support is available from Monday to Friday with the Computer Science Advanced Diploma course</li><li>Learn Computer Science Advanced Diploma skills at your own pace from the comfort of your home</li><li>Gain a complete understanding of Computer Science Advanced Diploma course</li><li>Accessible, informative Computer Science Advanced Diploma learning modules designed by expert instructors</li><li>Get 24/7 help or advice from our email and live chat teams with the Computer Science Advanced Diploma bundle</li><li>Study Computer Science Advanced Diploma in your own time through your computer, tablet or mobile device</li></ul><p>Our Computer Science Advanced Diploma bundle starts with the basics of Computer Science Advanced Diploma and gradually progresses towards advanced topics. Therefore, each lesson of this Computer Science Advanced Diploma is intuitive and easy to understand.</p><p><strong>Courses in this Computer Science Advanced Diploma Bundle:</strong></p><ul><li><strong>Course 01: </strong>Computer Science with Python</li><li><strong>Course 02: </strong>Diploma in Front-End Web Development (HTML, CSS, &amp; Bootstrap)</li><li><strong>Course 03: </strong>It: Ethical Hacking, IT Security and IT Cyber Attacking</li><li><strong>Course 04: </strong>Data Science and Visualisation with Machine Learning</li><li><strong>Course 05: </strong>Info Graphics Web Design - Affinity Designer Training</li><li><strong>Course 06:</strong> Website Hacking From Scratch</li><li><strong>Course 07: </strong>Web Application Penetration Testing Course</li><li><strong>Course 08: </strong>Excel Add-in with C# VSTO and Web</li><li><strong>Course 09: </strong>C++ Programming for Absolute Beginnerss</li><li><strong>Course 10: </strong>Computer Vision: C++ and OpenCV with GPU support</li><li><strong>Course 11: </strong>Learning Effective UX Designs</li><li><strong>Course 12: </strong>C++ Coding Masterclass</li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        120 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 Computer Science Advanced Diploma bundle helps aspiring professionals who want to obtain the knowledge and familiarise themselves with the skillsets to pursue a career in Computer Science Advanced Diploma.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>To enrol in this Computer Science Advanced Diploma course, all you need is a basic understanding of the English Language and an internet connection.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>The Computer Science Advanced Diploma bundle will enhance your knowledge and improve your confidence in exploring opportunities in various sectors related to Computer Science Advanced Diploma.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>12 CPD Accredited PDF Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Each CPD Accredited Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            £10.79
        </p>
            <div><p>CPD accredited hard copy certificates are available for £10.79 each</p><p><strong>Delivery Charge for Each Hard Copy Certificates:</strong></p><ul><li>Inside the UK: Free</li><li>Outside of the UK: £9.99 each</li></ul></div>

                        </div>

Compliance Central

Computer Science Advanced Diploma

<div id="course-pricing" fr-original-style="" style="box-sizing: border-box;">
          <p 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: rgb(0, 0, 0);">The Complete Python Course - Beginner to Expert is an extensive learning program designed to take participants from novice to proficient Python programmers. Throughout the course, learners will embark on a comprehensive journey covering fundamental Python concepts and gradually progressing to advanced topics.</span></p><p 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: rgb(0, 0, 0);">The course begins with an introduction to Python programming, providing an overview of its syntax, data types, and basic operations. Participants will gain a solid understanding of Python fundamentals, including variables, operators, and control flow structures.</span></p><p 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: rgb(0, 0, 0);">As the course progresses, learners will delve deeper into advanced Python concepts, such as error handling, functions, and object-oriented programming. They will explore the intricacies of Python libraries, including NumPy and Pandas, for data manipulation and analysis.</span></p><p 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: rgb(0, 0, 0);">Furthermore, participants will receive invaluable guidance on launching a successful career in Python, including tips on freelancing, networking, and building a strong professional brand. By the end of the course, learners will be equipped with the knowledge and skills to pursue various career opportunities in Python development, data analysis, machine learning, and more.</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 &nbsp;Complete Python Course - Beginner to Expert</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);">Gain a thorough grasp of Python programming from beginner to advanced levels.</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 Python syntax, covering comments, line structure, and indentation.</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 basic data types like strings, integers, floats, and booleans, and how to manipulate them.</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 Python operators, including comparison, arithmetic, and logical operators.</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);">Delve into advanced data types such as sets, lists, tuples, and dictionaries.</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);">Master control flow structures like conditional statements and loops.</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);">Develop proficiency in defining and using functions, understanding variable scope, and using docstrings.</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 user input and error handling techniques, including syntax errors and exceptions.</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 advanced topics like lambda functions, iterators, generators, and functional programming.</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);">Discover scripting and libraries usage, including IDEs, text editors, and importing scripts.</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 NumPy for numerical computing, including arrays and data 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);">Master data manipulation with Pandas, including dealing with missing values.</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);">Get an introduction to object-oriented programming (OOP) 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);">Explore advanced OOP topics like decorators, inheritance, and class methods.</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);">Receive guidance on starting a career in Python, including freelancing and networking.</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); box-sizing: border-box;" fr-original-style="color:#000000;">This &nbsp;Complete Python Course - Beginner to Expert Course is suitable for -</span></p></div><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);">Individuals new to programming</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 seeking Python proficiency</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 transitioning to Python</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 aiming for expertise in Python</li></ul><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;">Requirements</span></h2><div id="course-pricing" fr-original-style="" style="box-sizing: border-box;"><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);">Open to all interested in Python.</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);">&nbsp;No prerequisites; suitable for individuals from any academic background.</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);">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></h2></div><div fr-original-style="" style="box-sizing: border-box;"><div fr-original-style="" style="box-sizing: border-box;"><section fr-original-style="" style="box-sizing: border-box;"><div fr-original-style="" style="box-sizing: border-box;"><div fr-original-style="" style="box-sizing: border-box;"><div fr-original-style="" style="box-sizing: border-box;"><div fr-original-style="" style="box-sizing: border-box;"><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);">Python Developer</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);">Software 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);">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);">Web Developer</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 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);">Automation 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);">Cybersecurity Analyst</li></ul>
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Course Gate

Python Scripting

Unlock your potential in Python programming with our Complete Python Course - Beginner to Expert! From mastering syntax to advanced data analysis, machine learning, and web development, this comprehensive course equips you with essential programming skills. Start your journey today and become a proficient Python developer!

Complete Python Course - Beginner to Expert

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful.</p>Overview<p>Overview of data science and machine learning at scale<br />Overview of the Hadoop ecosystem<br />Working with HDFS data and Hive tables using Hue<br />Introduction to Cloudera Data Science Workbench<br />Overview of Apache Spark 2<br />Reading and writing data<br />Inspecting data quality<br />Cleansing and transforming data<br />Summarizing and grouping data<br />Combining, splitting, and reshaping data<br />Exploring data<br />Configuring, monitoring, and troubleshooting Spark applications<br />Overview of machine learning in Spark MLlib<br />Extracting, transforming, and selecting features<br />Building and evaluating regression models<br />Building and evaluating classification models<br />Building and evaluating clustering models<br />Cross-validating models and tuning hyperparameters<br />Building machine learning pipelines<br />Deploying machine learning models<br />Spark, Spark SQL, and Spark MLlib<br />PySpark and sparklyr<br />Cloudera Data Science Workbench (CDSW)<br />Hue</p><p>This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful.</p></div><div id="course-pills-2"><h4>Overview of data science and machine learning at scale</h4><h4>Overview of the Hadoop ecosystem</h4><h4>Working with HDFS data and Hive tables using Hue</h4><h4>Introduction to Cloudera Data Science Workbench</h4><h4>Overview of Apache Spark 2</h4><h4>Reading and writing data</h4><h4>Inspecting data quality</h4><h4>Cleansing and transforming data</h4><h4>Summarizing and grouping data</h4><h4>Combining, splitting, and reshaping data</h4><h4>Exploring data</h4><h4>Configuring, monitoring, and troubleshooting Spark applications</h4><h4>Overview of machine learning in Spark MLlib</h4><h4>Extracting, transforming, and selecting features</h4><h4>Building and evauating regression models</h4><h4>Building and evaluating classification models</h4><h4>Building and evaluating clustering models</h4><h4>Cross-validating models and tuning hyperparameters</h4><h4>Building machine learning pipelines</h4><h4>Deploying machine learning models</h4></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Cloudera Data Scientist Training training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Cloudera Data Scientist Training course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Data Engineering

Apache Spark

Big Data Analytics

Hadoop

Data Science

Data Skills

Cloudera Data Scientist Training

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			<h2>Overview
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					<p>Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career.</p><p>This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more.</p><p>After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today!</p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p><span>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.</span></p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p>This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science.</p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p>The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush.</p>					</div>
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			<h2>Career Path</h2>		</div>
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					<p>This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as</p><ul><li>Data Analyst </li><li>Data Scientist </li><li>Data Manager </li><li>Business Analyst</li></ul><p>And much more!</p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>90 sections • 90 lectures • 10:19:00 total length</p></div><div><div><div><div><div><div><div><p>•Course Overview &amp; Table of Contents: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•System and Environment preparation - Part 1: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•System and Environment preparation - Part 2: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of python - Assignment 1: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of python - Assignment 2: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of python - Functions: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of python - Data Structures: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of NumPy - NumPy Array: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of NumPy - NumPy Data: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of NumPy - NumPy Arithmetic: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of Matplotlib: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of Pandas - Part 1: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of Pandas - Part 2: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Understanding the CSV data file: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Load and Read CSV data file using Python Standard Library: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Load and Read CSV data file using NumPy: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Load and Read CSV data file using Pandas: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Summary - Peek, Dimensions and Data Types: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Summary - Class Distribution and Data Summary: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Summary - Explaining Correlation: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Visualization - Using Histograms: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Visualization - Using Density Plots: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Visualization - Box and Whisker Plots: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Multivariate Dataset Visualization - Correlation Plots: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Multivariate Dataset Visualization - Scatter Plots: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation (Pre-Processing) - Introduction: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Re-scaling Data - Part 1: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Re-scaling Data - Part 2: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Standardizing Data - Part 1: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Standardizing Data - Part 2: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Normalizing Data: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Binarizing Data: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Introduction: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Recursive Feature Elimination: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Principal Component Analysis (PCA): 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Feature Importance: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - Introduction: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - Train and Test Set: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Introduction: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Log Loss: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Classification Report: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Mean Square Error: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - R Squared: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - Logistic Regression: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - Naive Bayes: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - CART: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - Support Vector Machines: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Linear Regression: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Ridge Regression: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - CART: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Pipelines : Data Preparation and Data Modelling: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Pipelines : Feature Selection and Data Modelling: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Ensembles - Voting: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Ensembles - Bagging: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Ensembles - Boosting: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Parameter Tuning using Grid Search: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Parameter Tuning using Random Search: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Export, Save and Load Machine Learning Models : Pickle: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Export, Save and Load Machine Learning Models : Joblib: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Finalizing a Model - Introduction and Steps: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Resources - Data Science &amp; Machine Learning with Python: 00:00:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Computing Skills

Data Science with Python

<p><strong>***24 Hour Limited Time Flash Sale***</strong></p><p><strong> </strong><strong>Software Engineering: Master Programming, Web Design &amp; Development </strong><strong>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>Are you a professional feeling stuck in your career, struggling to keep up with the ever-changing demands of the industry? Or perhaps you're a beginner, unsure of where to start or how to break into your desired field. Whichever stage you're in, our exclusive Software Engineering: Master Programming, Web Design &amp; Development Bundle provides unique insights and tools that can help you achieve your goals.</p><p>Designed to cater to the needs of both seasoned professionals and aspiring newcomers, our Software Engineering: Master Programming, Web Design &amp; Development bundle is a comprehensive program that will equip you with the essential skills and knowledge you need to succeed. Whether you're looking to advance in your current role or embark on a new career journey, this bundle has everything you need to take your professional life to the next level.</p><p><strong>But that's not all. When you enrol in Software Engineering: Master Programming, Web Design &amp; Development Online Training, you'll receive 30 CPD-Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free.</strong></p><p><strong>Courses Are Included In this Software Engineering: Master Programming, Web Design &amp; Development Career Bundle:</strong></p><ul><li> Course 01: Coding with Scratch</li><li> Course 02: C# Programming - Beginner to Advanced</li><li> Course 03: Python Programming Bible</li><li> Course 04: Machine Learning with Python Course</li><li> Course 05: JavaScript Fundamentals</li><li> Course 06: Refactor Javascript</li><li> Course 07: Penetration Testing with OWASP ZAP</li><li> Course 08: Advanced SAS Programming Using MacrosSQL</li><li> Course 09: How To Startup Your Own SaaS (Software As a Service) Company (SaaS Evolution)</li><li> Course 10: Three.js &amp; WebGL 3D Programming</li><li> Course 11: Stripe with C#</li><li> Course 12: C++ Development: The Complete Coding Guide</li><li> Course 13: Bootstrap - Responsive Web Design</li><li> Course 14: Masterclass Bootstrap 5 Course - Responsive Web Design</li><li> Course 15: Diploma in User Experience UI/UX Design (Advanced)</li><li> Course 16: Advanced Diploma in PHP Web Development with MySQL, GitHub &amp; Heroku</li><li> Course 17: Node JS: API Development with Swagger</li><li> Course 18: HTML Web Development Crash Course</li><li> Course 19: CSS Web Development</li><li> Course 20: .NET Core API Development</li><li> Course 21: Graphic Design Using Canva</li><li> Course 22: Basics of WordPress: Create Unlimited Websites</li><li> Course 23: The Ultimate WordPress for Beginners Step-by-Step Blueprint</li><li> Course 24: Using WordPress for Affiliate Marketing</li><li> Course 25: WordPress 5.0 for Beginners to Advanced</li><li> Course 26: WordPress and SEO Online Course</li><li> Course 27: How To Build Easy Drag &amp; Drop Landing Pages With Wordpress</li><li> Course 28: Make a Wordpress WooCommerce Amazon Affiliate Store</li><li> Course 29: Wordpress Security Mastery</li><li> Course 30: How To Build a Wordpress Membership Site With Memberpress</li></ul><p>With Software Engineering: Master Programming, Web Design &amp; Development, you'll embark on an immersive learning experience that combines interactive lessons with voice-over audio, ensuring that you can learn from anywhere in the world, at your own pace. And with 24/7 tutor support, you'll never feel alone in your journey, whether you're a seasoned professional or a beginner.</p><p>Don't let this opportunity pass you by. Enrol in Software Engineering: Master Programming, Web Design &amp; Development today and take the first step towards achieving your goals and dreams.</p><p><strong>Why buy this Software Engineering: Master Programming, Web Design &amp; Development?</strong></p><ul><li><strong>Free CPD Accredited Certificate</strong> upon completion of Software Engineering: Master Programming, Web Design &amp; Development</li><li>Get <strong>a free student ID card</strong> with Software Engineering: Master Programming, Web Design &amp; Development</li><li><strong>Lifetime access</strong> to the Software Engineering: Master Programming, Web Design &amp; Development course materials</li><li>Get instant access to this Software Engineering: Master Programming, Web Design &amp; Development course</li><li>Learn Software Engineering: Master Programming, Web Design &amp; Development from anywhere in the world</li><li>24/7 tutor support with the Software Engineering: Master Programming, Web Design &amp; Development course.</li></ul> <p><strong>Start your learning journey straightaway with our Software Engineering: Master Programming, Web Design &amp; Development Training!</strong></p><p>Software Engineering: Master Programming, Web Design &amp; Development premium bundle consists of 30 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of the Software Engineering: Master Programming, Web Design &amp; Development is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience.</p><p><strong>Certification</strong></p><p>You have to complete the assignment given at the end of the Software Engineering: Master Programming, Web Design &amp; Development course. After passing the Software Engineering: Master Programming, Web Design &amp; Development exam You will be entitled to claim a PDF &amp; Hardcopy certificate accredited by CPD Quality standards completely free.</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><strong>This Software Engineering: Master Programming, Web Design &amp; Development course is ideal for:</strong></p><ul><li>Students seeking mastery in Software Engineering: Master Programming, Web Design &amp; Development</li><li>Professionals seeking to enhance Software Engineering: Master Programming, Web Design &amp; Development skills</li><li>Individuals looking for a Software Engineering: Master Programming, Web Design &amp; Development-related career.</li><li>Anyone passionate about Software Engineering: Master Programming, Web Design &amp; Development</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This <strong>Software Engineering: Master Programming, Web Design &amp; Development </strong>doesn't require prior experience and is suitable for diverse learners.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This <strong>Software Engineering: Master Programming, Web Design &amp; Development </strong>bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Digital Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>If you are an international student, you will be required to pay an additional fee of <strong>10 GBP</strong> for international delivery, and <strong>4.99 GBP</strong> for delivery within the UK, for each certificate</p></div>

                        </div>

Academy for Health and Fitness

Software Engineering: Master Programming, Web Design & Development

School of Business and Technology London

QUALIFI Level 7 Diploma in Data Science

<p><strong>24 Hour Flash Deal **25-in-1 Level 3 Diploma in ICT Information Systems and Principles for IT Professionals Mega Bundle**</strong><br /><br /><strong> Level 3 Diploma in ICT Information Systems and Principles for IT Professionals Enrolment Gifts  **FREE PDF Certificate**FREE PDF Transcript ** FREE Exam** FREE Student ID ** Lifetime Access **FREE Enrolment Letter **</strong><br /><br />Take the initial steps toward a successful long-term career by studying the <strong>Level 3 Diploma in ICT Information Systems and Principles for IT Professionals</strong> package online with <strong>Studyhub </strong>through our online learning platform. The Level 3 Diploma in ICT Information Systems and Principles for IT Professionals bundle can help you improve your CV, wow potential employers, and differentiate yourself from the mass.<br /><br />This <strong>Level 3 Diploma in ICT Information Systems and Principles for IT Professionals</strong> course provides complete 360-degree training on Level 3 Diploma in ICT Information Systems and Principles for IT Professionals. You'll get not one, not two, not three, but <strong>twenty-five</strong> Level 3 Diploma in ICT Information Systems and Principles for IT Professionals courses included in this course. Plus Studyhub's signature <strong>Forever Access</strong> is given as always, meaning these <strong>Level 3 Diploma in ICT Information Systems and Principles for IT Professionals</strong> courses are yours for as long as you want them once you enrol in this course<br /><br /><strong>This Level 3 Diploma in ICT Information Systems and Principles for IT Professionals Bundle consists the following career oriented courses:</strong></p><ul><li>Course 01: IT Skills for IT / Non - IT Professionals</li><li>Course 02: Computer Operating System and Troubleshooting</li><li>Course 03: IT Support Technician: Troubleshooting and Technical Expertise</li><li>Course 04: Computer Workstation Safety Training</li><li>Course 05: Foundations of IT Security: Protecting Digital Assets</li><li>Course 06: Cyber Security Awareness Training</li><li>Course 07: Cyber Security Incident Handling and Incident Response</li><li>Course 08: Information Governance and Data Management Training</li><li>Course 09: SQL Database Management</li><li>Course 10: Data Analytics</li><li>Course 11: Quick Data Science Approach from Scratch</li><li>Course 12: Machine Learning Basics</li><li>Course 13: Deep Learning Neural Network with R</li><li>Course 14: Cyber Security Law</li><li>Course 15: CEH: Learn Ethical Hacking From A-Z: Beginner To Expert</li><li>Course 16: Computer Networks Security from Scratch to Advanced</li><li>Course 17: CompTIA Network+ Certification (N10-007)</li><li>Course 18: Functional Skills IT/ ICT Level 2</li><li>Course 19: Internet of Things (IoT)</li><li>Course 20: Information Architecture (IA) Fundamentals for Website Design</li><li>Course 21: Learn Fundamentals of WordPress</li><li>Course 22: Digital Forensic Science: Unraveling Cybercrime Mysteries Course</li><li>Course 23: IT Administration and Networking</li><li>Course 24: Data Center Training Essentials: General Introduction</li><li>Course 25: Network Security and Risk Management</li></ul><p><strong>In this exclusive Level 3 Diploma in ICT Information Systems and Principles for IT Professionals bundle, you really hit the jackpot. 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It has been designed by our Level 3 Diploma in ICT Information Systems and Principles for IT Professionals specialists in a manner that you will be likely to find yourself head and shoulders above the others. For better learning, one to one assistance will also be provided if it's required by any learners.<br /><br />The <strong>Level 3 Diploma in ICT Information Systems and Principles for IT Professionals</strong> Bundle is one of the most prestigious training offered at StudyHub and is highly valued by employers for good reason. This Level 3 Diploma in ICT Information Systems and Principles for IT Professionals bundle course has been created with twenty-five premium courses to provide our learners with the best learning experience possible to increase their understanding of their chosen field.<br /><br />This <strong>Level 3 Diploma in ICT Information Systems and Principles for IT Professionals</strong> Course, like every one of Study Hub's courses, is meticulously developed and well researched. Every one of the topics is divided into Level 3 Diploma in ICT Information Systems and Principles for IT Professionals Elementary modules, allowing our students to grasp each lesson quickly.</p><p>The <strong>Level 3 Diploma in ICT Information Systems and Principles for IT Professionals</strong> course is self-paced and can be taken from the comfort of your home, office, or on the go! With our Student ID card you will get discounts on things like music, food, travel and clothes etc.</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">
                            <p>This <strong>Level 3 Diploma in ICT Information Systems and Principles for IT Professionals </strong>training is suitable for -</p><ul><li>Students</li><li>Recent graduates</li><li>Job Seekers</li><li>Individuals who are already employed in the relevant sectors and wish to enhance their knowledge and expertise in Level 3 Diploma in ICT Information Systems and Principles for IT Professionals</li><li>Further wants to study Pearson BTEC Level 2 Certificate in ICT Systems and Principles</li></ul><p><strong>Please Note:</strong> Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses.</p>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>To participate in this <strong>Level 3 Diploma in ICT Information Systems and Principles for IT Professionals </strong>course, all you need is -</p><ul><li>A smart device</li><li>A secure internet connection</li><li>And a keen interest in Level 3 Diploma in ICT Information Systems and Principles for IT Professionals</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>You will be able to kickstart your <strong>Level 3 Diploma in ICT Information Systems and Principles for IT Professionals </strong>career because this course includes various courses as a bonus. This bundle is an excellent opportunity for you to learn multiple skills from the convenience of your own home and explore Level 3 Diploma in ICT Information Systems and Principles for IT Professionals career opportunities</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><ul><li>CPD Accredited e-Certificate - Free</li><li>CPD Accredited Hardcopy Certificate - Free</li><li>Enrolment Letter - Free</li><li>Student ID Card - Free</li></ul></div>

                        </div>

Cert Iv

IT Certifications

Certificate in Education & Training (CET)

ICDL (International Certification of Digital Literacy)

Level 3 Diploma in ICT Information Systems and Principles for IT Professionals

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

DP-100T01 Designing and Implementing a Data Science Solution on Azure

Packt

Python

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Python Machine Learning Bootcamp

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Machine Learning with Real World Projects

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			<h2><b>Course Overview</b></h2>		</div>
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					<div><p><span>Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.</span></p><p><span>The </span><b>Machine Learning for Apps Level 4 </b><span>course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.</span></p><p><span>On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.</span></p></div>
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					<div><p>This best selling <strong>Machine Learning for Apps Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Machine Learning for Apps Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Machine Learning for Apps Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Machine Learning for Apps Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Machine Learning for Apps Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Machine Learning for Apps Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Machine Learning for Apps Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Course Cloud

Machine Learning (ML)

Machine Learning for Apps Level 4

<strong>The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.</strong>
        <p>Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects.

You'll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You'll learn to optimize this model and apply performance metrics to track its performance. You'll also learn topics like regression, classification, and clustering to improve the performance of your model. You'll learn the basics of neural networks and use scikit-learn to perform calculations in your project.

By the end of this course, you'll have created a face recognition application using everything you've learned in this course.

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</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. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.

Python Machine Learning Crash Course for Beginners

<strong>Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis</strong>
        <p>Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. 

In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. 

We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! 

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels</p>
        <h2>What You Will Learn</h2><p>Confidently work with two of the leading ML packages: statsmodels and sklearn <br>Understand how to perform a linear regression <br>Become familiar with the ins and outs of logistic regression <br>Get to grips with carrying out cluster analysis (both flat and hierarchical) <br>Apply your skills to real-life business cases <br>Get insights into the underlying ideas behind ML models</p>
        <h2>Audience</h2><p>If you want to get acquainted with fundamental machine learning methods, become a successful data scientist, or just get started with building valuable skills in machine learning and data science, this course is for you.</p>
        <h2>Approach</h2><p>The course has a perfect blend of theory and practical knowledge to help you gain a solid understand of the concepts covered.</p>
        <h2>Key Features</h2><p>Learn machine learning with StatsModels and sklearn  * Apply machine learning skills to solve real-world business cases  * Get started with linear regression, logistic regression, and cluster analysis</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-101-with-scikit-learn-and-statsmodels</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></p><br><h4>2. Setting Up the Working Environment</h4><p></p><br><h4>3. Linear Regression with StatsModels</h4><p></p><br><h4>4. Linear Regression with Sklearn</h4><p></p><br><h4>5. Linear Regression - Practical Example</h4><p></p><br><h4>6. Logistic Regression</h4><p></p><br><h4>7. Cluster Analysis</h4><p></p><br><h4>8. Cluster Analysis: Additional Topics</h4><p></p>

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

Machine Learning 101 with Scikit-learn and StatsModels

<strong>In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.</strong>
        <p>Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library.

The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms.

By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects.

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </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 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Machine Learning for Absolute Beginners - Level 2

<strong>Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!</strong>
        <p>Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
&bull;	Set up a Python development environment correctly
&bull;	Gain complete machine learning toolsets to tackle most real-world problems
&bull;	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
&bull;	Combine multiple models with by bagging, boosting or stacking
&bull;	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
&bull;	Develop in Jupyter (IPython) notebook, Spyder and various IDE
&bull;	Communicate visually and effectively with Matplotlib and Seaborn
&bull;	Engineer new features to improve algorithm predictions
&bull;	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
&bull;	Use SVM for handwriting recognition, and classification problems in general
&bull;	Use decision trees to predict staff attrition
&bull;	Apply the association rule to retail shopping datasets
&bull;	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Learn to Build Powerful Machine Learning Models to Solve Any Problem<br>&bull;&nbsp;&nbsp;Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells &amp; more</p>
        <h2>Audience</h2><p>A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.</p>
        <h2>Approach</h2><p>You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Solve any problem in your business or job with powerful Machine Learning models * &bull;&nbsp;&nbsp;Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/the-complete-machine-learning-course-with-python</p>
        <h2>About the Author</h2><h3>Anthony Ng</h3><p>Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. Getting Started with Anaconda</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Windows OS] Downloading & Installing Anaconda</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Downloading &amp; Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>2. [Windows OS] Managing Environment</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Managing Environment</p></td></tr><tr><td style='padding: 15px;'><p>3. Navigating the Spyder & Jupyter Notebook Interface</p><p style='margin: 0'>Getting Started with Anaconda: Navigating the Spyder &amp; Jupyter Notebook Interface</p></td></tr><tr><td style='padding: 15px;'><p>4. Downloading the IRIS Datasets</p><p style='margin: 0'>Getting Started with Anaconda: Downloading the IRIS Datasets</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Exploration and Analysis</p><p style='margin: 0'>Getting Started with Anaconda: Data Exploration and Analysis</p></td></tr><tr><td style='padding: 15px;'><p>6. Presenting Your Data</p><p style='margin: 0'>Getting Started with Anaconda: Presenting Your Data</p></td></tr></tbody></table><br><h4>3. Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Regression: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Categories of Machine Learning</p><p style='margin: 0'>Regression: Categories of Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Scikit-Learn</p><p style='margin: 0'>Regression: Working with Scikit-Learn</p></td></tr><tr><td style='padding: 15px;'><p>4. Boston Housing Data - EDA</p><p style='margin: 0'>Regression: Boston Housing Data - EDA</p></td></tr><tr><td style='padding: 15px;'><p>5. Correlation Analysis and Feature Selection</p><p style='margin: 0'>Regression: Correlation Analysis and Feature Selection</p></td></tr><tr><td style='padding: 15px;'><p>6. Simple Linear Regression Modelling with Boston Housing Data</p><p style='margin: 0'>Regression: Simple Linear Regression Modelling with Boston Housing Data</p></td></tr><tr><td style='padding: 15px;'><p>7. Robust Regression</p><p style='margin: 0'>Regression: Robust Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Evaluate Model Performance</p><p style='margin: 0'>Regression: Evaluate Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Regression with statsmodel</p><p style='margin: 0'>Regression: Multiple Regression with statsmodel</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Regression and Feature Importance</p><p style='margin: 0'>Regression: Multiple Regression and Feature Importance</p></td></tr><tr><td style='padding: 15px;'><p>11. Ordinary Least Square Regression and Gradient Descent</p><p style='margin: 0'>Regression: Ordinary Least Square Regression and Gradient Descent</p></td></tr><tr><td style='padding: 15px;'><p>12. Regularised Method for Regression</p><p style='margin: 0'>Regression: Regularised Method for Regression</p></td></tr><tr><td style='padding: 15px;'><p>13. Polynomial Regression</p><p style='margin: 0'>Regression: Polynomial Regression</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Non-linear relationships</p><p style='margin: 0'>Regression: Dealing with Non-linear relationships</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance Revisited</p><p style='margin: 0'>Regression: Feature Importance Revisited</p></td></tr><tr><td style='padding: 15px;'><p>16. Data Pre-Processing 1</p><p style='margin: 0'>Regression: Data Pre-Processing 1</p></td></tr><tr><td style='padding: 15px;'><p>17. Data Pre-Processing 2</p><p style='margin: 0'>Regression: Data Pre-Processing 2</p></td></tr><tr><td style='padding: 15px;'><p>18. Variance Bias Trade Off - Validation Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Validation Curve</p></td></tr><tr><td style='padding: 15px;'><p>19. Variance Bias Trade Off - Learning Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Learning Curve</p></td></tr><tr><td style='padding: 15px;'><p>20. Cross Validation</p><p style='margin: 0'>Regression: Cross Validation</p></td></tr></tbody></table><br><h4>4. Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Classification: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression 1</p><p style='margin: 0'>Classification: Logistic Regression 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression 2</p><p style='margin: 0'>Classification: Logistic Regression 2</p></td></tr><tr><td style='padding: 15px;'><p>4. MNIST Project 1 - Introduction</p><p style='margin: 0'>Classification: MNIST Project 1 - Introduction</p></td></tr><tr><td style='padding: 15px;'><p>5. MNIST Project 2 - SGDClassifiers</p><p style='margin: 0'>Classification: MNIST Project 2 - SGDClassifier</p></td></tr><tr><td style='padding: 15px;'><p>6. MNIST Project 3 - Performance Measures</p><p style='margin: 0'>Classification: MNIST Project 3 - Performance Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p><p style='margin: 0'>Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p></td></tr><tr><td style='padding: 15px;'><p>8. MNIST Project 5 - Precision and Recall Tradeoff</p><p style='margin: 0'>Classification: MNIST Project 5 - Precision and Recall Tradeoff</p></td></tr><tr><td style='padding: 15px;'><p>9. MNIST Project 6 - The ROC Curve</p><p style='margin: 0'>Classification: MNIST Project 6 - The ROC Curve</p></td></tr></tbody></table><br><h4>5. Support Vector Machine (SVM)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Support Vector Machine (SVM): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Support Vector Machine (SVM) Concepts</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear SVM Classification</p><p style='margin: 0'>Support Vector Machine (SVM): Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Polynomial Kernel</p><p style='margin: 0'>Support Vector Machine (SVM): Polynomial Kernel</p></td></tr><tr><td style='padding: 15px;'><p>5. Gaussian Radial Basis Function</p><p style='margin: 0'>Support Vector Machine (SVM): Gaussian Radial Basis Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Regression</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Advantages and Disadvantages of SVM</p><p style='margin: 0'>Support Vector Machine (SVM): Advantages and Disadvantages of SVM</p></td></tr></tbody></table><br><h4>6. Tree</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Tree: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Decision Tree</p><p style='margin: 0'>Tree: What is Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>3. Training a Decision Tree</p><p style='margin: 0'>Tree: Training a Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualising a Decision Trees</p><p style='margin: 0'>Tree: Visualising a Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>5. Decision Tree Learning Algorithm</p><p style='margin: 0'>Tree: Decision Tree Learning Algorithm</p></td></tr><tr><td style='padding: 15px;'><p>6. Decision Tree Regression</p><p style='margin: 0'>Tree: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Grid Search</p><p style='margin: 0'>Tree: Overfitting and Grid Search</p></td></tr><tr><td style='padding: 15px;'><p>8. Where to From Here</p><p style='margin: 0'>Tree: Where to From Here</p></td></tr><tr><td style='padding: 15px;'><p>9. Project HR - Loading and preprocesing data</p><p style='margin: 0'>Tree: Project HR - Loading and preprocesing data</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Modelling</p><p style='margin: 0'>Tree: Project HR - Modelling</p></td></tr></tbody></table><br><h4>7. Ensemble Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Ensemble Learning Methods Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble Learning Methods Introduction</p></td></tr><tr><td style='padding: 15px;'><p>3. Bagging Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Bagging Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>Ensemble Machine Learning: Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>6. Extra-Trees</p><p style='margin: 0'>Ensemble Machine Learning: Extra-Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. AdaBoost</p><p style='margin: 0'>Ensemble Machine Learning: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Machine</p><p style='margin: 0'>Ensemble Machine Learning: Gradient Boosting Machine</p></td></tr><tr><td style='padding: 15px;'><p>9. XGBoost</p><p style='margin: 0'>Ensemble Machine Learning: XGBoost</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Human Resources Analytics</p><p style='margin: 0'>Ensemble Machine Learning: Project HR - Human Resources Analytics</p></td></tr><tr><td style='padding: 15px;'><p>11. Ensemble of ensembles Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 1</p></td></tr><tr><td style='padding: 15px;'><p>12. Ensemble of ensembles Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 2</p></td></tr></tbody></table><br><h4>8. k-Nearest Neighbours (kNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. kNN Introduction</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. kNN Concepts</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. kNN and Iris Dataset Demo</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo</p></td></tr><tr><td style='padding: 15px;'><p>4. Distance Metric</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Distance Metric</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Cancer Detection Part 1</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Cancer Detection Part 2</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 2</p></td></tr></tbody></table><br><h4>9. Dimensionality Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Dimensionality Reduction: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Dimensionality Reduction Concept</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Concept</p></td></tr><tr><td style='padding: 15px;'><p>3. PCA Introduction</p><p style='margin: 0'>Dimensionality Reduction: PCA Introduction</p></td></tr><tr><td style='padding: 15px;'><p>4. Dimensionality Reduction Demo</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Demo</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Wine 1: Dimensionality Reduction with PCA</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Wine 2: Choosing the Number of Components</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 2: Choosing the Number of Components</p></td></tr><tr><td style='padding: 15px;'><p>7. Kernel PCA</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA</p></td></tr><tr><td style='padding: 15px;'><p>8. Kernel PCA Demo</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA Demo</p></td></tr><tr><td style='padding: 15px;'><p>9. LDA & Comparison between LDA and PCA</p><p style='margin: 0'>Dimensionality Reduction: LDA &amp; Comparison between LDA and PCA</p></td></tr></tbody></table><br><h4>10. Unsupervised Learning: Clustering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Unsupervised Learning: Clustering: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Clustering Concepts</p><p style='margin: 0'>Unsupervised Learning: Clustering: Clustering Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. MLextend</p><p style='margin: 0'>Unsupervised Learning: Clustering: MLextend</p></td></tr><tr><td style='padding: 15px;'><p>4. Ward's Agglomerative Hierarchical Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering</p></td></tr><tr><td style='padding: 15px;'><p>5. Truncating Dendrogram</p><p style='margin: 0'>Unsupervised Learning: Clustering: Truncating Dendrogram</p></td></tr><tr><td style='padding: 15px;'><p>6. k-Means Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: k-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>7. Elbow Method</p><p style='margin: 0'>Unsupervised Learning: Clustering: Elbow Method</p></td></tr><tr><td style='padding: 15px;'><p>8. Silhouette Analysis</p><p style='margin: 0'>Unsupervised Learning: Clustering: Silhouette Analysis</p></td></tr><tr><td style='padding: 15px;'><p>9. Mean Shift</p><p style='margin: 0'>Unsupervised Learning: Clustering: Mean Shift</p></td></tr></tbody></table>

TensorFlow

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!

The Complete Machine Learning Course with Python

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					<div><p>The comprehensive Machine Learning - Use in Flutter has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Machine Learning - Use in Flutter today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Machine Learning - Use in Flutter has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning - Use in Flutter is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Machine Learning - Use in Flutter is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Machine Learning - Use in Flutter is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Machine Learning - Use in Flutter, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning - Use in Flutter will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Machine Learning - Use in Flutter to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Learning & Development

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Machine Learning - Use in Flutter

<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>

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.

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