Name: Deep Learning Neural Network with R Course
Price: 12 GBP
Availability: InStock

<p><strong>Data analysis</strong> is a crucial skill in today's data-driven world, and the demand for skilled data analysts in the UK is on the rise. According to the Office for National Statistics (ONS), the UK's digital economy grew by <strong>7.9% in this year</strong>, creating numerous job opportunities in fields such as data analysis.</p><p>In Data Analysis in Excel with Analytics for Data Analysts course, you will get to learn the fundamentals of Excel data analysis. The highly informative modules will help you enhance your understanding of charts, pivot tables, pivot charts and more. You will acquire the expertise to work with multiple workbooks and worksheets. In this step-by-step learning process, you will get to grasp a better understanding of lookup functions. In addition, you will know the essentials of data forecasting, mapping data and formula auditing.</p><p>At the end of the data analysis course, you can receive QLS endorsed certificate of achievement. This certificate will allow you to enhance your resume and lift your employability. Enrol now!</p><p><strong>Dis Diploma Included Courses are: </strong></p><ul><li><strong>Course 01: Diploma in Data Analytics with Tableau<br /></strong></li><li><strong>Course 02: Data Analytics<br /></strong></li><li><strong>Course 03: Google Data Studio: Data Analytics</strong><strong><br /></strong></li><li><strong>Course 04: Big Data Analytics with PySpark Tableau Desktop and MongoDB<br /></strong></li><li><strong>Course 05: Learn MySQL from Scratch for Data Science and Analytics</strong><strong><br /></strong></li><li><strong>Course 06: Data Analysis In Excel<br /></strong></li><li><strong>Course 07: Diploma in Data Analysis Fundamentals<br /></strong></li><li><strong>Course 08: Business Data Analysis<br /></strong></li><li><strong>Course 09: Data Science &amp; Machine Learning with Python<br /></strong></li><li><strong>Course 10: R Programming for Data Science<br /></strong></li><li><strong>Course 11: Learn Python, JavaScript, and Microsoft SQL for Data science</strong></li></ul><p><strong>Key Learnings:</strong></p><ul><li>Ability to effectively modify and organize data within a worksheet using various formatting tools.</li><li>Proficiency in working with lists and using data analysis techniques such as sorting, filtering, and subtotalling.</li><li>Competency in analysing data through the use of functions, such as statistical and mathematical functions, to derive meaningful insights.</li><li>Proficiency in creating visually appealing charts and graphs to effectively communicate data insights.</li><li>Competency in using PivotTables and PivotCharts to efficiently summarize and analyse large sets of data.</li><li>Proficiency in automating data analysis tasks and functions through the use of macros and other automated workbook functionalities.</li></ul><p><strong>Course Significant Points:</strong></p><ul><li>Quality Licence Scheme Endorsed</li><li>CPD Accredited Course</li><li>Unlimited Retake Exam &amp; Tutor Support</li><li>Easy Accessibility to the Course Materials</li><li>100% Learning Satisfaction Guarantee</li><li>Lifetime Access &amp; 24/7 Support</li><li>Self-paced online course Modules</li><li>Covers to Explore Multiple Job Positions</li></ul><p><strong>How is the course assessed?</strong></p><p>To assess your learning, you have to complete the assignment questions provided at the end of the course. You have to score at least 60% to pass the exam and to qualify for Quality Licence Scheme endorsed and CPD-accredited certificates. After passing the assignment exam, you will be able to apply for a certificate.</p><p><strong>Meet the Endorsement</strong></p><p>The Quality Licence Scheme has been designed specifically to recognise high-quality courses. This Data Analysis with Excel course materials are recognised by Quality Licence Scheme (QLS). This ensures the deep research and quality resource allocation behind the development phase of the course. In addition, the QLS certificate enriches your CV and recognises your quality study on the relevant subject.</p><p><strong>How will QLS boost your worth?</strong></p><ul><li>Achieving <strong>Quality Licence Scheme</strong> endorsed certificates can provide you with valued and credible evidence of <strong>your skills and knowledge to potential employers. </strong></li><li>Quality Licence Scheme (QLS) endorsements are awarded to training programmes and courses that <strong>meet high quality standards</strong>, ensuring that your certificate represents <strong>a valuable achievement.</strong></li><li>The stringent assessment process involved in achieving Quality Licence Scheme Endorsed Certificates ensures that you have acquired the <strong>necessary skills and knowledge to excel in your chosen field.</strong></li></ul><p><strong>Meet the Accreditation</strong></p><p>CPD Quality Standards (CPD QS) accreditation assures the Data Analysis with Excel course training and learning activities are relevant, reliable, and up to date.</p><p><strong>How Will CPD Accredited Value You?</strong></p><ul><li>CPD accreditation ensures that the training programme or course you have completed has been <strong>independently reviewed</strong> and meets <strong>rigorous quality standards.</strong></li><li>By achieving CPD accredited certificates, you can <strong>enhance your career prospects</strong>, build your <strong>professional reputation</strong>, and stay competitive in <strong>today's job market.</strong></li></ul><p><strong>**Diploma in Data Analytics with Tableau**</strong></p><ul><li>Introduction To The Course</li><li>Project 1: Discount Mart (Sales And Profit Analytics)</li><li>Project 2: Green Destinations (HR Analytics)</li><li>Project 3: Superstore (Sales Agent Tracker)</li><li>Northwind Trade (Shipping Analytics)</li><li>Project 5: Tesla (Stock Price Analytics)</li><li>Bonus: Introduction To Database Concepts</li><li>Tableau Stories</li></ul><p><strong>**Data Analytics**</strong></p><ul><li>Introduction To The World Of Data</li><li>Basics Of Data Analytics</li><li>Statistics For Data Analytics</li><li>Actions Taken In The Data Analysis Process</li><li>Gathering The Right Information</li><li>Storing Data</li><li>Data Mining</li><li>Excel For Data Analytics</li><li>Tools For Data Analytics</li><li>Data-Analytic Thinking</li><li>Data Visualisation That Clearly Describes Insights</li><li>Data Visualisation Tools</li></ul><p><strong>**Google Data Studio: Data Analytics**</strong></p><ul><li>Introduction</li><li>Google Sheets</li><li>Google Data Studio</li></ul><p><strong>**Big Data Analytics with PySpark Tableau Desktop and MongoDB**</strong></p><ul><li>Introduction</li><li>Setup And Installations</li><li>Data Processing With PySpark And MongoDB</li><li>Machine Learning With PySpark And MLlib</li><li>Creating The Data Pipeline Scripts</li><li>Tableau Data Visualization</li><li>Source Code</li></ul><p><strong>**Learn MySQL from Scratch for Data Science and Analytics**</strong></p><ul><li>Getting Started</li><li>SQL Server Setting Up</li><li>SQL Database Basics</li><li>SQL DML (Data Manipulation Language)</li><li>SQL DDL (Data Definition Language)</li><li>SQL DCL (Data Control Language)</li><li>SQL Statement Basic</li><li>Filtering Data Rows</li><li>Aggregate Functions For Data Analysis</li><li>SQL Data Analyticstatements</li><li>SQL Group By Statement</li><li>JOINS</li><li>SQL Constraints</li><li>Views</li><li>Advanced SQL Functions</li><li>SQL Stored Procedures</li><li>Import &amp; Export Data</li><li>Backup And Restore Database</li></ul><p><strong>**Data Analysis In Excel**</strong></p><ul><li>Modifying A Worksheet</li><li>Working With Lists</li><li>Analyzing Data</li><li>Visualizing Data With Charts</li><li>Using PivotTables And PivotCharts</li><li>Working With Multiple Worksheets And Workbooks</li><li>Using Lookup Functions And Formula Auditing</li><li>Automating Workbook Functionality</li><li>Creating Sparklines And Mapping Data</li><li>Forecasting Data</li></ul><p><strong>**Diploma in Data Analysis Fundamentals**</strong></p><ul><li>Introduction</li><li>Agenda And Principles Of Process Management</li><li>The Voice Of The Process</li><li>Working As One Team For Improvement</li><li>Exercise: The Voice Of The Customer</li><li>Tools For Data Analysis</li><li>The Pareto Chart</li><li>The Histogram</li><li>The Run Chart</li><li>Exercise: Presenting Performance Data</li><li>Understanding Variation</li><li>The Control Chart</li><li>Control Chart Example</li><li>Control Chart Special Cases</li><li>Interpreting The Control Chart</li><li>Control Chart Exercise</li><li>Strategies To Deal With Variation</li><li>Using Data To Drive Improvement</li><li>A Structure For Performance Measurement</li><li>Data Analysis Exercise</li><li>Course Project</li><li>Test Your Understanding</li></ul><p><strong>**Business Data Analysis**</strong></p><ul><li>Introduction To Business Analysis</li><li>Business Environment</li><li>Business Processes</li><li>Business Analysis Planning And Monitoring</li><li>Strategic Analysis And Product Scope</li><li>Solution Evaluation</li><li>Investigation Techniques</li><li>Ratio Analysis</li><li>Stakeholder Analysis And Management</li><li>Process Improvement With Gap Analysis</li><li>Documenting And Managing Requirements</li><li>Business Development And Succession Planning</li><li>Planning &amp; Forecasting Operations</li><li>Business Writing Skills</li></ul><p><strong>**Data Science &amp; Machine Learning with Python**</strong></p><ul><li>Course Overview &amp; Table Of Contents</li><li>Introduction To Machine Learning</li><li>System And Environment Preparation</li><li>Learn Basics Of Python - Assignment</li><li>Learn Basics Of Python - Functions</li><li>Learn Basics Of Python - Data Structures</li><li>Learn Basics Of NumPy - NumPy Array</li><li>Learn Basics Of NumPy - NumPy Data</li><li>Learn Basics Of NumPy - NumPy Arithmetic</li><li>Learn Basics Of Matplotlib</li><li>Learn Basics Of Pandas</li><li>Understanding The CSV Data File</li><li>Load And Read CSV Data File Using Python Standard Library</li><li>Load And Read CSV Data File Using NumPy</li><li>Load And Read CSV Data File Using Pandas</li><li>Dataset Summary - Peek, Dimensions And Data Types</li><li>Dataset Summary - Class Distribution And Data Summary</li><li>Dataset Summary - Explaining Correlation</li><li>Dataset Summary - Explaining Skewness - Gaussian And Normal Curve</li><li>Dataset Visualization - Using Histograms</li><li>Dataset Visualization - Using Density Plots</li><li>Dataset Visualization - Box And Whisker Plots</li><li>Multivariate Dataset Visualization - Correlation Plots</li><li>Multivariate Dataset Visualization - Scatter Plots</li><li>Data Preparation (Pre-Processing) - Introduction<br /><ul><li>Re-Scaling Data</li><li>Standardizing Data</li><li>Normalizing Data</li><li>Binarizing Data</li></ul></li><li>Feature Selection - Introduction<br /><ul><li>Uni-Variate Part</li><li>Recursive Feature Elimination</li><li>Principal Component Analysis (PCA)</li><li>Feature Importance</li></ul></li><li>Refresher Session - The Mechanism Of Re-Sampling, Training And Testing</li><li>Algorithm Evaluation Techniques - Introduction<br /><ul><li>Train And Test Set</li><li>K-Fold Cross Validation</li><li>Leave One Out Cross Validation</li><li>Repeated Random Test-Train Splits</li></ul></li><li>Algorithm Evaluation Metrics - Introduction<br /><ul><li>Classification Accuracy</li><li>Log Loss</li><li>Area Under ROC Curve</li><li>Confusion Matrix</li><li>Classification Report</li><li>Mean Absolute Error - Dataset Introduction</li><li>Mean Absolute Error</li><li>Mean Square Error</li><li>R Squared</li></ul></li><li>Classification Algorithm Spot Check - Logistic Regression<br /><ul><li>Linear Discriminant Analysis</li><li>K-Nearest Neighbors</li><li>Naive Bayes</li><li>CART</li><li>Support Vector Machines</li></ul></li><li>Regression Algorithm Spot Check - Linear Regression<br /><ul><li>Ridge Regression</li><li>Lasso Linear Regression</li><li>Elastic Net Regression</li><li>K-Nearest Neighbors</li><li>CART</li><li>Support Vector Machines (SVM)</li></ul></li><li>Compare Algorithms</li><li>Pipelines</li><li>Performance Improvement: Ensembles - Voting<br /><ul><li>Bagging</li><li>Boosting</li></ul></li><li>Performance Improvement: Parameter Tuning Using Grid Search</li><li>Performance Improvement: Parameter Tuning Using Random Search</li><li>Export, Save And Load Machine Learning Models : Pickle</li><li>Export, Save And Load Machine Learning Models : Joblib</li><li>Finalizing A Model - Introduction And Steps</li><li>Finalizing A Classification Model - The Pima Indian Diabetes Dataset</li><li>Quick Session: Imbalanced Data Set - Issue Overview And Steps</li><li>Iris Dataset : Finalizing Multi-Class Dataset</li><li>Finalizing A Regression Model - The Boston Housing Price Dataset</li><li>Real-Time Predictions: Using The Pima Indian Diabetes Classification Model</li><li>Real-Time Predictions: Using Iris Flowers Multi-Class Classification Dataset</li><li>Real-Time Predictions: Using The Boston Housing Regression Model</li></ul><p><strong>**R Programming for Data Science**</strong></p><ul><li>Data Science Overview</li><li>R And RStudio</li><li>Introduction To Basics</li><li>Vectors</li><li>Matrices</li><li>Factors</li><li>Data Frames</li><li>Lists</li><li>Relational Operators</li><li>Logical Operators</li><li>Conditional Statements</li><li>Loops</li><li>Functions</li><li>R Packages</li><li>The Apply Family - Lapply</li><li>The Apply Family - Sapply &amp; Vapply</li><li>Useful Functions</li><li>Regular Expressions</li><li>Dates And Times</li><li>Getting And Cleaning Data</li><li>Plotting Data In R</li><li>Data Manipulation With Dplyr</li></ul><p><strong>**Learn Python, JavaScript, and Microsoft SQL for Data science**</strong></p><ul><li>JavaScript Introduction</li><li>JavaScript Basics</li><li>JavaScript Operators</li><li>JavaScript Conditional Statements</li><li>JavaScript Control Flow Statements</li><li>JavaScript Functions</li><li>JavaScript Error Handling</li><li>JavaScript Client-Side Validations</li><li>Python Introduction</li><li>Python Basic</li><li>Python Strings</li><li>Python Operators</li><li>Python Data Structures</li><li>Python Conditional Statements</li><li>Python Control Flow Statements</li><li>Python Core Games</li><li>Python Functions</li><li>Python Args, KW Args For Data Science</li><li>Python Project</li><li>Python Object Oriented Programming [OOPs]</li><li>Python Methods</li><li>Python Class And Objects</li><li>Python Inheritance And Polymorphism</li><li>Python Encapsulation And Abstraction</li><li>Python OOPs Games</li><li>Python Modules And Packages</li><li>Python Error Handling</li><li>Microsoft SQL[MS] Introduction</li><li>MS SQL Statements</li><li>MS SQL Filtering Data</li><li>MS SQL Functions</li><li>MS SQL Joins</li><li>MS SQL Advanced Commands</li><li>MS SQL Structure And Keys</li><li>MS SQL Queries</li><li>MS SQL Structure Queries</li><li>MS SQL Constraints</li><li>MS SQL Backup And Restore</li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        570 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>Data Analysis course is ideal for: </strong></p><ul><li>Professionals working in fields such as finance, business, and healthcare who need to analyse data and make decisions based on their findings.</li><li>Students and researchers who want to develop their skills in data analysis and visualisation using Excel.</li><li>Individuals who are looking to pursue a career in data analysis and want to gain a foundational understanding of Excel and its capabilities.</li><li>Business owners and entrepreneurs who want to learn how to use Excel to analyse and interpret data for their own businesses.</li><li>Anyone who wants to improve their data analysis skills for personal or professional purposes and gain proficiency in Excel as a tool for data analysis.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no specific prerequisites to enrol in Data analysis course. <strong> Anyone and everyone</strong> can take this course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>The course will assist you in establishing a solid understanding of data analysis. After the course, you will be able to explore career options such as:</p><ul><li><strong>Data Analyst </strong>(Average annual salary £35,000)</li><li><strong>Research Analyst</strong> (Average annual salary £28,552)</li><li><strong>Business Analyst</strong> (Average annual salary £42,618)</li><li><strong>Risk Analyst </strong>(Average annual salary £41,340)</li><li><strong>Financial Analyst</strong> (Average annual salary £50,300)</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion (PDF)</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p><strong>CPD Accredited PDF Certificate.</strong><br />A PDF Certificate: £9</p> </div>
        <h4>Certificate of completion (Hardcopy)</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p><strong>CPD Accredited Hard Copy Certificate. </strong><br />A Hardcopy Certificate (within the UK): £15<br />A Hardcopy Certificate (international): £15 + £10 (postal charge) = £25</p> </div>
        <h4>Endorsed Certificate of Achievement from the Quality Licence Scheme (Hardcopy)</h4>
        <p>
            Hard copy certificate -
            £109
        </p>
            <div> <p>Data Analysis In Excel (within the UK: £109, International: £119)</p> </div>

                        </div>

One Education

Data Analyst : Data Analyst (Data Analytics) - Level 5 Diploma Course

General Admission

<p><strong>"Start your career as an AWS Cloud Architect</strong><strong> and earn up to £70,000 Per Year."</strong></p><p>Dreaming of a high-paying career in the booming tech industry? Look no further than Cloud Architecture! The UK cloud computing market is expected to reach a staggering <strong>£172 billion by 2025</strong>, creating a surge in demand for skilled professionals. Salaries for AWS Cloud Architects average a lucrative<strong> £65,000</strong>, and the best part? You can launch your career without a formal degree. So, are you ready to take control of your future?</p><p>Our AWS Cloud Architect Job Ready Program is your all-in-one solution to learn cloud computing from scratch and also secure a job in this exciting industry. With our unique blend of comprehensive courses and career support, your dream job is within reach. We understand the challenges of transitioning into a new field, which is why our program is tailored to guide you every step of the way - from mastering core skills to landing your first job in cloud computing. By choosing our AWS Cloud Architect Job Ready Program, you're not just gaining skills; you're gaining a lifelong career partner.</p><p><strong>Our mission is simple - to be your trusted partner every step of the way, from Training to Employment.</strong></p><p>Enrol Now and Kickstart Your AWS Cloud Architect Career. Don't miss this opportunity to transform your career with the AWS Cloud Architect Job Ready Program. With our comprehensive training, career support, and money-back guarantee, you're not just enrolling in a program; you're investing in a brighter future. Join us now and become a part of the next generation of AWS Cloud Architect!</p><p>In addition to teaching you the technical skills you need, we will also provide you with career mentoring and support. We will help you build your resume, prepare for interviews, and land your dream job. We also have partnerships with many companies that are hiring AWS Cloud Architect, so we can help you get the job you want.</p><p>If you are not happy with our service, we also offer a 100% money-back guarantee. So what are you waiting for? Enrol in our AWS Cloud Architect Job Ready Program today and start your journey to becoming a successful AWS Cloud Architect!</p><p><strong>Why Choose Us?</strong></p><p>So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our AWS Cloud Architect Job Ready Program:</p><p><strong>Personalised Guidance:</strong> Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some experience, we will guide you towards honing your skills and developing a strong foundation in cloud computing.</p><p><strong>One-On-One Consultation Sessions with Experts:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the cloud computing field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.</p><p><strong>Extensive Job Opportunities:</strong> We have established partnerships with numerous companies actively seeking AWS Cloud Architects. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere. Our aim is to maximise your employment prospects and provide you with a range of exciting opportunities to choose from.</p><p><strong>Interview Preparation:</strong> We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.</p><p><strong>Money-Back Guarantee:</strong> Your satisfaction is our top priority. We are confident in the quality of our training and support, which is why we offer a 100% money-back guarantee.</p><p><strong>Courses we will provide once you enrol in the program:</strong></p><ul><li><strong>Course 01: </strong>Online AWS Certified Database Specialty Exam Preparation (DBS-C01)</li><li><strong>Course 02: </strong>AWS Essentials Online Course</li><li><strong>Course 03: </strong>AWS Certified Solutions Architect Associate</li><li><strong>Course 04: </strong>Microsoft Azure Cloud Concepts</li><li><strong>Course 05: </strong>Azure Administrator Essentials</li><li><strong>Course 06: </strong>Fundamentals of Azure Virtual Desktop</li><li><strong>Course 07: </strong>Learn Azure AD PowerShell for M365</li><li><strong>Course 08: </strong>Azure Masterclass</li><li><strong>Course 09: </strong>Azure Machine Learning</li><li><strong>Course 10: </strong>Google Cloud for Beginners</li><li><strong>Course 11: </strong>Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li><strong>Course 12: </strong>CompTIA Network (N10-007)</li><li><strong>Course 13: </strong>CompTIA Security+ (SY0-601)</li><li><strong>Course 14: </strong>Introduction to Virtual Apps and Virtual Desktop Solution</li><li><strong>Course 15: </strong>Cryptography</li><li><strong>Course 16: </strong>Business and Data Analytics for Beginners</li><li><strong>Course 17: </strong>Python from Scratch</li><li><strong>Course 18: </strong>Coding Essentials - Javascript, ASP. Net, C# - Bonus HTML</li><li><strong>Course 19: </strong>Practical Project Management</li><li><strong>Course 20: </strong>Cyber Security Advanced Training</li></ul> <p>The AWS Cloud Architect Job Ready Program with Career Support &amp; Money Back Guarantee is completed in 9 easy steps:</p><p><strong>Step 1: Enrol in the Programme</strong></p><p>Begin your exciting journey with us by enrolling in the AWS Cloud Architect Job Ready Program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.</p><p>After you enrol in the Program, you will get lifetime access to 20 premium courses related to cloud computing. These courses will teach you the knowledge and skills required to become a successful AWS Cloud Architect. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!</p><p><strong>Step 2: Initial One-On-One Counselling Session</strong></p><p>Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.</p><p><strong>Step 3: Certification upon Course Completion</strong></p><p>After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.</p><p><strong>Step 4: CV Revamping</strong></p><p>Our team of professionals will build you a compelling CV and LinkedIn profile. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the cloud computing industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently.</p><p><strong>Step 5: Building Network and Submitting CV</strong></p><p>We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many high-profile individuals and companies through your LinkedIn profile.</p><p><strong>Step 6: Interview Preparation</strong></p><p>With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.</p><p><strong>Step 7: Securing Job Interviews</strong></p><p>Leveraging our partnerships with leading companies, we'll secure job interviews for you. We'll ensure you get the opportunity to showcase your skills to potential employers and get the dream job you want.</p><p><strong>Step 8: Post-Interview Support</strong></p><p>Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.</p><p><strong>Step 9: Celebrate Your New Job!</strong></p><p>Once you've secured your dream job in cloud computing, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.</p><p><br />We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful AWS Cloud Architect career begins with us.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        100 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 AWS Cloud Architect Job Ready Program is perfect for:</strong></p><ul><li>Beginners eager to break into the field of cloud computing and get a job.</li><li>Professionals looking to move their career towards data science.</li><li>Individuals seeking a comprehensive understanding of data analysis tools and techniques.</li><li>Those aiming for a career upgrade with advanced analytics skills.</li><li>Anyone interested in gaining practical, job-ready skills in Cloud Architecture.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No experience is required. Just enrol &amp; start learning.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p><strong>This AWS Cloud Architect Job Ready Program will help you get a job in different roles, such as:</strong></p><ul><li>Cloud Architect: £65,000+</li><li>Cloud Engineer: £55,000+</li><li>DevOps Engineer: £50,000+</li><li>Solutions Architect: £70,000+</li><li>Cloud Security Engineer: £60,000+</li><li>Data Architect: £62,000+</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited e-Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Framed (Hardcopy) Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Enrolment Letter</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>QLS Endorsed Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Academy for Health and Fitness

AWS Certification

AWS Certified Developer

Cloud Architecture

AWS Certified Cloud Practitioner

AWS (Amazon Web Services)

AWS Certified Solutions Architect

Cloud (AWS, Azure, Google) Architect Job Ready Program with Money Back Guarantee

<p><strong>Earn up to 75k as a 'IT Technician' or Your 100% Money Back</strong></p><p>Thinking about a career in IT? The UK tech sector is booming, with over <strong>2 million</strong> IT professionals employed and a projected growth of <strong>12%</strong> in the next five years! The average IT Technician salary is £35,000, making it a lucrative and rewarding career choice. But where do you start? Our IT Technician Courses Including Job Opportunity Programme is here to guide you all the way until you get the job you want.</p><p>Our IT Technician Courses Including Job Opportunity Programme is your <strong>all-in-one solution</strong> to learn from scratch and also <strong>secure a job</strong> in this exciting industry. With our unique blend of comprehensive courses and career support, your dream job is within reach. We understand the challenges of transitioning into a new field, which is why our program is tailored to guide you every step of the way - from mastering core skills to landing your first job as a IT technician. By choosing our IT Technician Courses Including Job Opportunity Programme, you're not just gaining skills; <strong>you're gaining a </strong><strong>lifelong career partner</strong>.</p><p><strong>Our mission is simple</strong> - to be your trusted partner every step of the way, from training to employment.</p><p>In addition to teaching you the technical skills you need, we will also provide you with <strong>career mentoring and support</strong>. We will help you build your resume, prepare for interviews, and land your <strong>dream job</strong>. We also have<strong> partnerships with many companies</strong> that are hiring Programmers, so we can help you get your foot in the door.</p><p>If you are not happy with our service, we also offer a <strong>100% money-back guarantee</strong>. So what are you waiting for? Enrol in our IT Technician Courses Including Job Opportunity today and start your journey to becoming a successful IT Technician!</p><p><strong>Why Choose Us?</strong></p><p>So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Certified IT Technician Courses:</p><ul><li><strong>Personalised Guidance:</strong> We believe in the power of individual attention. Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some IT experience, we will guide you towards honing your skills and developing a strong foundation in IT.<br /><br /></li><li><strong>One-On-One Consultation:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the IT Technician field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.<br /><br /></li><li><strong>Extensive Job Opportunities:</strong> We have established partnerships with numerous companies actively seeking Certified IT Technicians. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere.<br /><br /></li><li><strong>Interview Preparation:</strong> No more stress over unexpected interview questions. We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.</li></ul><p><strong>Courses we will provide once you enrol in the program:</strong></p><ul><li><strong>Course 01: </strong>IT Technician</li><li><strong>Course 02: </strong>Functional Skills IT</li><li><strong>Course 03: </strong>Data Center Training Essentials: General Introduction</li><li><strong>Course 04: </strong>Understanding Data Protection and Data Security</li><li><strong>Course 05: </strong>CompTIA Network</li><li><strong>Course 06: </strong>CompTIA IT Fundamentals ITF+ (FCO-U61)</li><li><strong>Course 07: </strong>CompTIA A+ (220-1001)</li><li><strong>Course 08: </strong>CompTIA A+ (220-1002)</li><li><strong>Course 09: </strong>CompTIA Network+ Certification (N10-007)</li><li><strong>Course 10: </strong>CompTIA Security+ (SY0-601)</li><li><strong>Course 11: </strong>Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li><strong>Course 12: </strong>CompTIA CySA+ Cybersecurity Analyst (CS0-002)</li><li><strong>Course 13: </strong>CompTIA PenTest+ (Ethical Hacking)</li><li><strong>Course 14: </strong>Secure Programming of Web Applications</li><li><strong>Course 15: </strong>Document Control</li><li><strong>Course 16: </strong>Information Management</li><li><strong>Course 17: </strong>Cyber Security Law</li><li><strong>Course 18: </strong>Cyber Security Incident Handling and Incident Response</li></ul><p><strong>How Can We Offer Job Guarantees?</strong></p><p>HF Online provides consultancy, professional and educational services to many of the companies in the UK. During our intense exclusive training program, you will not just gain and complete the industry valuable certification but will gain industry experience as well, which is imperative to get your 1st job in the sector.</p> <p><strong>The is </strong><strong> completed in 9 easy steps:</strong></p><p><strong>Step 1: Enrol in the Programme</strong></p><p>Begin your exciting journey with us by enrolling in the IT Technician Courses. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.</p><p>After you enrol in the Program, you will get lifetime access to<strong> 18 Premium Courses</strong> related to T Technician. These courses will teach you the knowledge and skills required to become a successful T Technician. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!</p><p><strong>Step 2: Initial One-On-One Counselling Session</strong></p><p>Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.</p><p><strong>Step 3 - </strong><strong>Certification upon Course Completion</strong></p><p>After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.</p><p><strong>Step 4: </strong><strong>CV Revamping</strong></p><p>Our team of professionals will build you a compelling CV and <strong>LinkedIn profile</strong>. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the programming/development industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently.</p><p><strong>Step 5: Building Network and Submitting CV</strong></p><p>We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many<strong> high-profile individuals and companies</strong> through your LinkedIn profile.</p><p><strong>Step 6: Interview Preparation</strong></p><p>With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.</p><p><strong>Step 7: Securing Job Interviews</strong></p><p>Leveraging our partnerships with leading companies, <strong>we'll secure job interviews for you</strong>. We'll ensure you get the opportunity to showcase your skills to potential employers and <strong>get the dream job you want</strong>.</p><p><strong>Step 8: Post-Interview Support</strong></p><p>Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.</p><p><strong>Step 9: Celebrate Your New Job!</strong></p><p>Once you've secured your dream job in IT , it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.</p><p>We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career as an IT Technician begins with us.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        100 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 IT Technician Course Including the Job Opportunity Course is suitable for:</p><ul><li>Beginners with no prior programming experience who are looking to enter the world of coding.</li><li>Intermediate programmers who wish to expand their skills and explore IT's extensive usage.</li><li>IT professionals are seeking to diversify their skill sets and stay relevant in today's tech-driven market.</li><li>Data analysts and scientists who want to leverage IT's powerful libraries for their data processing needs.</li><li>Students and recent graduates are looking to enhance their employability in the thriving tech industry.</li><li>Entrepreneurs who wish to understand and utilise IT to automate tasks and analyse data for their businesses.</li><li>Anyone with a keen interest in IT and a desire to delve into its applications in diverse fields like web development, data science, machine learning, and more.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No experience required. Just enrol &amp; start learning.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>IT Technician: £20,000 - £50,000 annually</li><li>Network Administrator: £25,000 - £70,000 annually</li><li>Cyber Security Analyst: £35,000 - £85,000 annually</li><li>Data Center Technician: £30,000 - £75,000 annually</li><li>Cloud Solutions Architect: £40,000 - £100,000 annually</li><li>Ethical Hacker/Penetration Tester: £35,000 - £90,000 annually</li><li>Information Security Manager: £45,000 - £110,000 annually</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited e-Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Framed (Hardcopy) Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Enrolment Letter</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>QLS Endorsed Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

IT Technician

CompTIA Network+

IT Certifications

Computer Technician

CompTIA A+

CompTIA Security+

IT Technician Courses Including Job Opportunity

<p><strong>Earn up to 90k as a 'Network Engineer' or Your 100% Money Back</strong></p><p>Network outages cost UK businesses <strong>£100 billion</strong> annually. With over <strong>50,000 </strong>open network engineer jobs paying <strong>£54,000</strong> per year on average, skilled professionals are in high demand. Do you have what it takes to design, build, and protect company infrastructure?</p><p>This Network Engineer Job Guarantee Programme is your <strong>all-in-one</strong> solution to learn networking skills from scratch and also<strong> secure a job</strong> in this exciting industry. With our unique blend of comprehensive courses and career support, your dream job is within reach. We understand the challenges of transitioning into a new field, which is why our program is tailored to guide you every step of the way - from mastering core skills to landing your first job as a Network Engineer. By choosing our Network Engineer Job Guarantee Programme, you're not just gaining skills; <strong>you're gaining a lifelong career partner.</strong></p><p><strong>Our mission is simple</strong> - to be your trusted partner every step of the way, from training to employment.</p><p>In addition to teaching you the technical skills you need, we will also provide you with <strong>career mentoring and support</strong>. We will help you build your resume, prepare for interviews, and land your <strong>dream job</strong>. We also have<strong> partnerships with many companies</strong> that are hiring Programmers, so we can help you get your foot in the door.</p><p>If you are not happy with our service, we also offer a <strong>100% money-back guarantee</strong>. So what are you waiting for? Enrol in our Network Engineer Job Guarantee Programme today and start your journey to becoming a successful Network Engineer!</p><p><strong>Why Choose Us?</strong></p><p>So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Certified Network Engineer Programme:</p><ul><li><strong>Personalised Guidance:</strong> We believe in the power of individual attention. Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some Network Engineering experience, we will guide you towards honing your skills and developing a strong foundation in Network Engineering.<br /><br /></li><li><strong>One-On-One Consultation:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the Network Engineering field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.<br /><br /></li><li><strong>Extensive Job Opportunities:</strong> We have established partnerships with numerous companies actively seeking Certified Network Engineers. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere.<br /><br /></li><li><strong>Interview Preparation:</strong> No more stress over unexpected interview questions. We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.</li></ul><p><strong>Courses we will provide once you enrol in the program:</strong></p><ul><li><strong>Course 01: </strong>Network Security Level 2</li><li><strong>Course 02: </strong>IGCSE ICT: Network Essentials</li><li><strong>Course 03: </strong>Computer Networks Security</li><li><strong>Course 04: </strong>CompTIA Networking</li><li><strong>Course 05: </strong>AWS Certified Cloud Practitioner</li><li><strong>Course 06: </strong>IT Support Technician</li><li><strong>Course 07: </strong>CompTIA A+ (220-1001)</li><li><strong>Course 08: </strong>CompTIA Network (N10-007)</li><li><strong>Course 09: </strong>CompTIA A+ (220-1002)</li><li><strong>Course 10: </strong>CompTIA IT Fundamentals ITF+ (FCO-U61)</li><li><strong>Course 11: </strong>CompTIA PenTest+ (Ethical Hacking)</li><li><strong>Course 12: </strong>Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li><strong>Course 13: </strong>Cisco ASA Firewall Training</li><li><strong>Course 14: </strong>CompTIA Network+ Certification</li><li><strong>Course 15: </strong>Penetration Testing with OWASP ZAP: Mastery course</li><li><strong>Course 16: </strong>GDPR Data Protection</li><li><strong>Course 17: </strong>Data Center Training Essentials: Mechanical &amp; Cooling</li><li><strong>Course 18: </strong>Cyber Security Awareness Training</li></ul><p><strong>How Can We Offer Job Guarantees?</strong></p><p>HF Online provides consultancy, professional and educational services to many of the companies in the UK. During our intense exclusive training program, you will not just gain and complete the industry valuable certification but will gain industry experience as well, which is imperative to get your 1st job in the sector.</p> <p><strong>The is </strong><strong> completed in 9 easy steps:</strong></p><p><strong>Step 1: Enrol in the Programme</strong></p><p>Begin your exciting journey with us by enrolling in the Network Engineer Job Guarantee Programme. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.</p><p>After you enrol in the Program, you will get lifetime access to<strong> 18 Premium Courses</strong> related to Network Engineering. These courses will teach you the knowledge and skills required to become a successful Network Engineer. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about anything!</p><p><strong>Step 2: Initial One-On-One Counselling Session</strong></p><p>Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.</p><p><strong>Step 3 - </strong><strong>Certification upon Course Completion</strong></p><p>After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.</p><p><strong>Step 4: </strong><strong>CV Revamping</strong></p><p>Our team of professionals will build you a compelling CV and <strong>LinkedIn profile</strong>. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the programming/development industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently.</p><p><strong>Step 5: Building Network and Submitting CV</strong></p><p>We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many<strong> high-profile individuals and companies</strong> through your LinkedIn profile.</p><p><strong>Step 6: Interview Preparation</strong></p><p>With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.</p><p><strong>Step 7: Securing Job Interviews</strong></p><p>Leveraging our partnerships with leading companies, <strong>we'll secure job interviews for you</strong>. We'll ensure you get the opportunity to showcase your skills to potential employers and <strong>get the dream job you want</strong>.</p><p><strong>Step 8: Post-Interview Support</strong></p><p>Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.</p><p><strong>Step 9: Celebrate Your New Job!</strong></p><p>Once you've secured your dream job in Network Engineering, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.</p><p>We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Network Engineering begins with us.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        100 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 Network Engineering Course is suitable for:</p><ul><li>Beginners with no prior programming experience who are looking to enter the world of coding.</li><li>Intermediate programmers who wish to expand their skills and explore Network Engineering's extensive usage.</li><li>IT professionals seeking to diversify their skill sets and stay relevant in today's tech-driven market.</li><li>Data analysts and scientists who want to leverage Network Engineering's powerful libraries for their data processing needs.</li><li>Students and recent graduates looking to enhance their employability in the thriving tech industry.</li><li>Entrepreneurs who wish to understand and utilise Network Engineering to automate tasks and analyse data for their businesses.</li><li>Anyone with a keen interest in Network Engineering and a desire to delve into its applications in diverse fields like web development, data science, machine learning, and more.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No experience required. Just enrol &amp; start learning.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Network Engineer: £30,000 - £70,000 annually</li><li>Network Security Analyst: £35,000 - £85,000 annually</li><li>Cloud Solutions Architect: £40,000 - £100,000 annually</li><li>IT Support Specialist: £25,000 - £55,000 annually</li><li>Cyber Security Consultant: £40,000 - £90,000 annually</li><li>Systems Administrator: £30,000 - £75,000 annually</li><li>Penetration Tester: £35,000 - £95,000 annually</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited e-Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Framed (Hardcopy) Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Enrolment Letter</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>QLS Endorsed Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Network & Security

Network Engineer

IT Networking Fundamentals

Computer Networking

Clinical Engineer

Software Developer

Web Development

Full Stack Developer

Web Developer

Back End Web Development

Web Design

Web Developer Job Guarantee Programme

<p>Give a compliment to your career and take it to the next level. This<strong> Data Science </strong>will provide you with the essential knowledge to shine in your professional career. Whether you want to develop skills for your next job or elevate your skills for your next promotion, this Data Sciencebundle will help you stay ahead of the pack.</p><p>Throughout the Data Scienceprogramme, it stresses how to improve your competency as a person in your chosen field while also outlining essential career insights in the relevant job sector.</p><p>Along with this Data Science course, you will get <strong>10 premium courses, an original</strong><strong>Hardcopy</strong>, <strong>11 PDF Certificates (Main Course + Additional Courses) </strong>Student ID card as gifts.</p><p><strong>This Data Science Bundle Consists of the following Premium courses: </strong></p><ul><li><strong> Course 01: </strong>Data Science with Python</li><li><strong> Course 02: </strong>Data Science &amp; Machine Learning with Python</li><li><strong> Course 03: </strong>R Programming for Data Science</li><li><strong> Course 04: </strong>Python for Data Analysis</li><li><strong> Course 05: </strong>Machine Learning for Predictive Maps in Python and Leaflet</li><li><strong> Course 06: </strong>Learn to Use Python for Spatial Analysis in ArcGIS</li><li><strong> Course 07: </strong>Python Programming for Everybody</li><li><strong> Course 08: </strong>Encryption</li><li><strong> Course 09: </strong>GDPR Data Protection Level 5</li><li><strong> Course</strong> 10: Decision Making and Critical Thinking</li><li><strong> Course 11: </strong>Time Management</li></ul><p>Enrol now in Data Science to advance your career, and use the premium study materials from Apex Learning.<br /><strong>Certificate:</strong></p><ul><li><strong>PDF Certificate: Free (Previously it was £6*11 = £66)</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course: Previously it was £10)</strong></li></ul><p>The bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your Data Scienceexpertise and essential knowledge, which will assist you in reaching your goal.</p><p><strong>Curriculum:</strong></p><p><strong> Course 01: R Programming for Data Science</strong></p><ul><li>Unit 01: Data Science Overview</li><li>Unit 02: R and RStudio</li><li>Unit 03: Introduction to Basics</li><li>Unit 04: Vectors</li><li>Unit 05: Matrices</li><li>Unit 06: Factors</li><li>Unit 07: Data Frames</li><li>Unit 08: Lists</li><li>Unit 09: Relational Operators</li><li>Unit 10: Logical Operators</li><li>Unit 11: Conditional Statements</li><li>Unit 12: Loops</li><li>Unit 13: Functions</li><li>Unit 14: R Packages</li><li>Unit 15: The Apply Family - lapply</li><li>Unit 16: The apply Family - sapply &amp; vapply</li><li>Unit 17: Useful Functions</li><li>Unit 18: Regular Expressions</li><li>Unit 19: Dates and Times</li><li>Unit 20: Getting and Cleaning Data</li><li>Unit 21: Plotting Data in R</li><li>Unit 22: Data Manipulation with dplyr</li></ul><p>And much more...</p><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>Anyone from any background can enrol in this Data Science bundle.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Data Science 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>R Programming for Data Science</strong>) absolutely Free! Other Hard Copy certificates are available for £10 each. </p><p>Please Note: 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

Data Science with Python Course

<strong>Kickstart your journey into deep learning and gain a strong understanding of deep neural networks through practical exercises. Develop your intuition and learn the fundamentals of artificial neural networks, activation functions, and loss functions. Gain practical experience with Python and TensorFlow 2.x, and apply your skills to build powerful deep learning models.</strong>
        <p>Unlock the power of deep learning and take your machine learning skills to the next level with our comprehensive course on deep neural networks.

This hands-on course will provide you with a solid understanding of the fundamentals of deep learning, including artificial neural networks, activation functions, bias, data, and loss functions. You will learn the basics of Python, with a focus on data science, as well as the essential tools for cleaning and examining data, plotting with Matplotlib, and working with NumPy and Pandas.

With this foundation in place, you will dive deep into the world of deep learning, starting with the MP Neuron model and progressing to the Perceptron, the Sigmoid Neuron, and the Universal Approximation Theorem. You will explore common activation functions, such as ReLU and SoftMax, and learn how to apply them in real-world applications.

Through a series of practical exercises, you will gain hands-on experience with TensorFlow 2.x, one of the most popular deep learning frameworks in use today. You will learn how to create and train deep neural networks, evaluate their performance, and fine-tune them for optimal results.

By the end of the course, you will be well on your way to becoming a deep learning expert in no time.</p>
        <h2>What You Will Learn</h2><p>Learn about the fundamentals of Python and some of its well-known libraries<br>Understand the fundamentals of deep learning and neural networks<br>Build and train your own deep neural network models<br>Learn different activation functions and optimization algorithms<br>Learn techniques for improving model performance and reducing overfitting<br>Apply deep learning to real-world problems in various fields</p>
        <h2>Audience</h2><p>This course is suitable for anyone interested in exploring the field of deep learning and building a solid foundation in artificial neural networks. No prior experience in programming or machine learning is required, making it an ideal starting point for beginners. It is ideal for students, professionals, and anyone who wants to enhance their skills and stay up-to-date with the latest developments in the field of artificial intelligence. Whether you are looking to kickstart your career or simply want to explore the exciting world of deep learning, this course is a great choice.</p>
        <h2>Approach</h2><p>The course adopts a practical, hands-on learning approach that focuses on building intuition and mastery through implementation. Students will work through a series of exercises and projects designed to help them understand the fundamental concepts of deep learning and develop their skills in Python programming, data analysis, and neural network design.</p>
        <h2>Key Features</h2><p>Practical hands-on exercises to build intuition on deep neural networks * Learn essential concepts in deep learning through practical applications * A comprehensive understanding of neural network architecture and design</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Deep-Learning---Crash-Course-2023</p>
        <h2>About the Author</h2><h3>Manifold AI Learning</h3><p>Manifold AI Learning is an online academy with the goal to empower students with the knowledge and skills that can be directly applied to solving real-world problems in data science, machine learning, and artificial intelligence.

With a curated curriculum and a hands-on guide, you will always be an industry-ready professional.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><br><h4>1. Welcome on Board</h4><p></p><br><h4>2. Getting the Basics Right</h4><p></p><br><h4>3. Python Crash Course on Basics</h4><p></p><br><h4>4. Python for Data Science - Crash Course</h4><p></p><br><h4>5. MP Neuron Model</h4><p></p><br><h4>5. MP Neuron Model</h4><p></p><br><h4>6. MP Neuron in Python</h4><p></p><br><h4>7. Summary of MP Neuron</h4><p></p><br><h4>7. Summary of MP Neuron</h4><p></p><br><h4>8. Perceptron</h4><p></p><br><h4>9. Perceptron in Python</h4><p></p><br><h4>10. Sigmoid Neuron</h4><p></p><br><h4>11. Sigmoid Neuron Implement with Python</h4><p></p><br><h4>12. Basic Probability</h4><p></p><br><h4>12. Basic Probability</h4><p></p><br><h4>13. Why Deep Neural Networks - Intuition</h4><p></p><br><h4>13. Deep Neural Networks</h4><p></p><br><h4>14. Universal Approximation Theorem</h4><p></p><br><h4>15. Deep Learning with TensorFlow 2.x</h4><p></p><br><h4>16. Activation Functions in Deep Learning Neural Networks</h4><p></p><br><h4>17. Applying Deep Learning</h4><p></p>

Packt

Keras

Data Science

Python

Pandas

TensorFlow

Deep Learning

Kickstart your journey into deep learning and gain a strong understanding of deep neural networks through practical exercises. Develop your intuition and learn the fundamentals of artificial neural networks, activation functions, and loss functions. Gain practical experience with Python and TensorFlow 2.x, and apply your skills to build powerful deep learning models.

Deep Learning - Crash Course 2023

<p>The world of machine learning and data analysis is rapidly evolving, and the demand for skilled professionals is soaring. If you're looking to level up your skills in this field, then this Deep Learning Neural Network with R course is just what you need. This course is designed to help you master the fundamentals of deep learning neural networks, with a specific focus on R programming.</p><p>Through this course, you'll learn how to build single-layer and multi-layer neural networks, which are critical for many machine learning applications. You'll start by diving into single-layer neural networks for agricultural projects, where you'll learn how to develop models to predict crop yields. Then, you'll move on to multi-layer neural networks for war casualties analysis, where you'll learn how to identify patterns and make predictions.</p><p>With hands-on projects and real-world examples, you'll gain the practical experience and theoretical understanding you need to take on complex deep learning projects. Whether you're an experienced data analyst or just starting in the field, this course is perfect for anyone looking to expand their knowledge and expertise in deep learning neural networks with R.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Understanding of single-layer and multi-layer neural networks</li><li>Knowledge of R programming for neural network applications</li><li>Implementation of neural networks in real-world projects</li><li>Familiarity with agriculture and war datasets for neural network modelling</li><li>Ability to evaluate neural network model accuracy and performance</li></ul><p>The Deep Learning Neural Network with R course is designed to provide learners with a comprehensive understanding of how to build and evaluate neural networks using R programming language. The course includes four modules that cover single-layer and multi-layer neural networks applied to agriculture and war datasets. Each module contains practical hands-on projects that allow learners to gain real-world experience in neural network development and evaluation. By the end of the course, learners will have a solid understanding of neural network concepts, R programming language, and practical experience with real-world datasets.</p><p><strong>â±â±Deep Learning Neural Network with R</strong><strong> Course Curriculum </strong></p><ul><li><strong>Section 01: </strong>Single Layer Neural Networks Project - Agriculture (Part - 1)</li><li><strong>Section 02: </strong>Single Layer Neural Networks Project - Agriculture (Part - 2)</li><li><strong>Section 03: </strong>Multi-Layer Neural Networks Project - Deaths in wars (Part - 1)</li><li><strong>Section 04: </strong>Multi-Layer Neural Networks Project - Deaths in wars (Part - 2)</li></ul><p><strong>How is the course assessed?</strong></p><p>Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.</p><p><strong>Exam &amp; Retakes:</strong><br />It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.</p><p><strong>Certification</strong><br />Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</p><h2>
        
        <div>CPD</div>
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                                        10 CPD hours / points
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                                            Accredited by CPD Quality Standards
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        <div>Who is this course for?</div>
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                        <div id="who_is_this_course_for">
                            <ul><li>Data analysts and scientists seeking to expand their knowledge of neural networks and R programming</li><li>Professionals interested in applying neural networks to agriculture or war datasets</li><li>Students and researchers interested in deep learning and machine learning techniques</li><li>Anyone looking to enhance their skills in data analysis and modelling using neural networks and R programming</li></ul>
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        <div>Requirements</div>
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                        <div id="requirements">
                            <p>There are no formal entry requirements for the course, with enrollment open to anyone!</p>
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        <div>Career path</div>
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                        <div id="career_path">
                            <ul><li>Data Analyst</li><li>Machine Learning Engineer</li><li>Data Scientist</li><li>Artificial Intelligence Developer</li><li>Research Scientist</li></ul><p>Entry-level positions such as Data Analysts can expect to earn between £25,000 to £35,000 per annum, whereas senior-level positions such as Machine Learning Engineers can earn upwards of £70,000 per annum.</p>
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        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

Deep Learning Neural Network with R Course

<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>

Nexus Human

Data Engineering

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

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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					<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>

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

Learning To Learn

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>

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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