Name: Data Analytics Using Python Visualizations
Price: 41.99 GBP
Availability: InStock

<p>In today's digital world, data is the new oil, and it has revolutionized the way we live, work, and communicate. The world is moving towards a more data-driven approach, and it's becoming increasingly important to equip yourself with the necessary skills to stay ahead of the curve. That's where our Advanced Diploma in Data Science &amp; Machine Learning with R at QLS Level 7 comes in.</p><p>The history of data science dates back to the 1960s, but it wasn't until the 21st century that it truly began to flourish. Today, it has become an integral part of businesses, governments, and non-profit organizations around the world. The Advanced Diploma in Data Science &amp; Machine Learning with R at QLS Level 7 is designed to provide you with the knowledge and skills needed to succeed in this exciting and rapidly growing field.</p><p>This comprehensive program includes three QLS-endorsed courses, namely the 2021 Data Science &amp; Machine Learning with R from A-Z, Statistics &amp; Probability for Data Science &amp; Machine Learning, and Machine Learning with Python. These courses cover everything from data manipulation and visualization to predictive analytics and machine learning algorithms. You will learn how to use R, Python, and other popular data science tools and techniques to analyze and extract insights from complex datasets.</p><p>In addition to the three QLS-endorsed courses, this program also includes five CPD QS accredited relevant courses and five CPD QS accredited career development courses. These courses cover a range of topics, including data visualization, deep learning, and big data analytics, among others. Upon completion of this program, you will be equipped with the knowledge and skills needed to pursue a rewarding career in data science and machine learning.</p><p>This bundle is a testament to the high quality of education that can be achieved through QLS-endorsed courses. With three such courses included in this bundle, learners are sure to receive a top-notch education that is both informative and engaging.</p><p>Investing in your education is always a wise choice, and with 3 QLS Endorsed certificates to your name, you can be sure that you're double boosting your resume.</p><p>Apart from the QLS courses, the learners will get access to 10 CPD courses that are in line with the subject matter. These will enhance your overall understanding as well as broaden your knowledge.</p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01:</strong>Advanced Diploma in Data Science &amp; Machine Learning with R at QLS Level 7</li><li><strong>Course 02:</strong>Advanced Diploma in Statistics &amp; Probability for Data Science &amp; Machine Learning at QLS Level 7</li><li><strong>Course 03:</strong>Certificate in Machine Learning with Python at QLS Level 3</li></ul><p><strong>CPD QS Accredited Courses:</strong></p><ul><li><strong>Course 04:</strong> Learn MySQL from Scratch for Data Science and Analytics</li><li><strong>Course 05:</strong> Azure Machine Learning</li><li><strong>Course 06:</strong> Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 07:</strong> Machine Learning Model Using AWS SageMaker Canvas</li><li><strong>Course 08:</strong> Diploma in Python Programming</li></ul><p>Take your career to the next level with our bundle that includes not only technical courses but also <strong>five guided courses</strong> focused on personal development and <strong> career growth.</strong></p><ul><li><strong>Course 09: </strong>Leadership and Management Course</li><li><strong>Course 10:</strong> Career Development and Passion</li><li><strong>Course 11: </strong>Professional CV Writing</li><li><strong>Course 12: </strong>Boost Your Confidence and Self-Esteem</li><li><strong>Course 13: </strong>Public Speaking: Confident Delivery Skills</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Understand the key concepts and techniques of data science and machine learning</li><li>Learn to manipulate and analyze data using R and Python</li><li>Gain proficiency in statistical analysis and probability theory</li><li>Develop skills in data visualization and creating reports with R Markdown</li><li>Explore regression and classification algorithms for predictive modeling</li><li>Build web applications with R Shiny and machine learning models using AWS SageMaker</li><li>Gain knowledge in lean six sigma tools and quality management principles</li></ul><p>The Advanced Diploma in Data Science &amp; Machine Learning with R at QLS Level 7 is a comprehensive program that covers key topics and techniques in data science and machine learning. The course modules provide learners with the necessary skills to manipulate and analyze data using R and Python, statistical analysis, data visualization, and predictive modeling using regression and classification algorithms. Learners will also gain experience in building web applications with R Shiny and machine learning models using AWS SageMaker. The course is suitable for those looking to start a career in data science or enhance their skills in this area.</p><p><strong>Certification</strong></p><p>Learners will qualify for the <strong>highly sought-after QLS Endorsed Certificate</strong> after successfully passing the assessment at the conclusion of each endorsed course.</p><p><strong>The Quality Licence Scheme</strong> is a credible and renowned endorsement that marks <strong>high quality</strong><strong>and excellence</strong> in the UK. It is well accepted and sought after by industry experts and recruiters.</p><p>Learners who purchase this bundle will receive <strong>Three Hardcopy QLS Certificates at their door!</strong></p><h2>
        
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                                        40 CPD hours / points
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                                            Accredited by CPD Quality Standards
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                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Professionals looking to enhance their skills in data science and machine learning</li><li>Individuals interested in starting a career in data science or machine learning</li><li>Students pursuing degrees in computer science, mathematics, or related fields</li></ul>
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    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst (£24,000 - £44,000)</li><li>Data Scientist (£35,000 - £75,000)</li><li>Machine Learning Engineer (£45,000 - £100,000)</li><li>Business Intelligence Analyst (£25,000 - £60,000)</li><li>Quality Manager (£30,000 - £70,000)</li></ul>
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        <div>Certificates</div>
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                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

One Education

Advanced Diploma in Data Science & Machine Learning with R at QLS Level 7 Course

General Admission

NextGen Learning

Data Science & Machine Learning with Python Course

<p><strong>***24 Hour Limited Time Flash Sale***</strong></p><p><strong> </strong><strong>Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma </strong><strong>Admission Gifts </strong></p><p><strong> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter</strong></p><p>Are you a professional feeling stuck in your career, struggling to keep up with the ever-changing demands of the industry? Or perhaps you're a beginner, unsure of where to start or how to break into your desired field. Whichever stage you're in, our exclusive Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma Bundle provides unique insights and tools that can help you achieve your goals.</p><p>Designed to cater to the needs of both seasoned professionals and aspiring newcomers, our Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma bundle is a comprehensive program that will equip you with the essential skills and knowledge you need to succeed. Whether you're looking to advance in your current role or embark on a new career journey, this bundle has everything you need to take your professional life to the next level.</p><p><strong>But that's not all. When you enrol in Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma Online Training, you'll receive 20 CPD-Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free.</strong></p><p><strong>Courses Are Included In this Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma Career Bundle:</strong></p><ul><li> Course 01: Statistics for Data Science &amp; Machine Learning</li><li> Course 02: Data Science &amp; Machine Learning with R Training</li><li> Course 03: R Programming for Data Science</li><li> Course 04: Statistical Concepts in R</li><li> Course 05: Learn Python, JavaScript, and Microsoft SQL for Data science</li><li> Course 06: Data Science &amp; Machine Learning with Python</li><li> Course 07: Spatial Data Visualization and Machine Learning in Python</li><li> Course 08: Azure Machine Learning</li><li> Course 09: Python Data Science</li><li> Course 10: Python Data Science with Numpy, Pandas and Matplotlib</li><li> Course 11: Quick Data Science Approach from Scratch</li><li> Course 12: Learn MySQL from Scratch for Data Science and Analytics</li><li> Course 13: Hands on Machine Learning Project - Auto Image Captioning for Social Media</li><li> Course 14: Statistical Analysis</li><li> Course 15: Statistics Essentials</li><li> Course 16: Data Analysis In Excel</li><li> Course 17: Microsoft Excel Course for Everyone - Complete Excel Course</li><li> Course 18: Introduction to Discrete Maths</li><li> Course 19: Advanced Mathematics Training</li><li> Course 20: Computer Simulation of Realistic Mathematical Models</li></ul><p>With Level 7 in Strategic Learning &amp; Development, you'll embark on an immersive learning experience that combines interactive lessons with voice-over audio, ensuring that you can learn from anywhere in the world, at your own pace. And with 24/7 tutor support, you'll never feel alone in your journey, whether you're a seasoned professional or a beginner.</p><p>Don't let this opportunity pass you by. Enrol in Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma today and take the first step towards achieving your goals and dreams.</p><p><strong>Why buy this Level 7 in Strategic Learning &amp; Development?</strong></p><ul><li><strong>Free CPD Accredited Certificate</strong> upon completion of Level 7 in Strategic Learning &amp; Development</li><li>Get <strong>a free student ID card</strong> with Level 7 in Strategic Learning &amp; Development</li><li><strong>Lifetime access</strong> to the Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma course materials</li><li>Get instant access to this Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma course</li><li>Learn Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma from anywhere in the world</li><li>24/7 tutor support with the Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma course.</li></ul> <p><strong>Start your learning journey straightaway with our Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma Training!</strong></p><p>Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma premium bundle consists of 20 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of the Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience.</p><p><strong>Certification</strong></p><p>You have to complete the assignment given at the end of the Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma course. After passing the Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma exam You will be entitled to claim a PDF &amp; Hardcopy certificate accredited by CPD Quality standards completely free.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        200 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 Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma course is ideal for:</strong></p><ul><li>Students seeking mastery in Level 7 in Strategic Learning &amp; Development</li><li>Professionals seeking to enhance Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma skills</li><li>Individuals looking for a Level 7 in Strategic Learning &amp; Development-related career.</li><li>Anyone passionate about Level 7 in Strategic Learning &amp; Development</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This <strong>Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma </strong>doesn't require prior experience and is suitable for diverse learners.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This <strong>Statistics for Data Science &amp; Machine Learning at QLS Level 7 Advanced Diploma </strong>bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Academy for Health and Fitness

Learning & Development

Statistics for Data Science & Machine Learning at QLS Level 7 Advanced Diploma

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					<p><span>This comprehensive course on <b>Data Science &amp; Machine Learning with R</b> will deepen your understanding on this topic.</span></p><p><span>After successful completion of this course you can acquire the required skills in this sector. This <b>Data Science &amp; Machine Learning with R</b> comes with accredited certification, which will enhance your CV and make you worthy in the job market.</span></p><p><span>So enrol in this course today to fast track your career ladder.</span></p>					</div>
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					<p>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.<br /></p>					</div>
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					<p><span>There is no experience or previous qualifications required for enrolment on this <b>Data Science &amp; Machine Learning with R</b>. It is available to all students, of all academic backgrounds.</span></p>					</div>
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					<p><span>Our <b>Data Science &amp; Machine Learning with R</b> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</span></p><p><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></p>					</div>
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					<p><span>Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business &amp; Management, Admin, Accountancy &amp; Finance, Secretarial &amp; PA, Teaching &amp; Mentoring etc.</span></p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>10 sections • 69 lectures • 22:07:00 total length</p></div><div><div><div><div><div><div><div><p>•Data Science and Machine Learning Introduction: 00:03:00</p></div><div><div><p>•What is Data Science: 00:10:00</p></div><div><div><p>•Machine Learning Overview: 00:05:00</p></div><div><div><p>•Who is This Course for: 00:03:00</p></div><div><div><p>•Data Science and Machine Learning Marketplace: 00:05:00</p></div><div><div><p>•Data Science and Machine Learning Job Opportunities: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Getting Started: 00:11:00</p></div><div><div><p>•Basics: 00:06:00</p></div><div><div><p>•Files: 00:11:00</p></div><div><div><p>•RStudio: 00:07:00</p></div><div><div><p>•Tidyverse: 00:05:00</p></div><div><div><p>•Resources: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Unit Introduction: 00:30:00</p></div><div><div><p>•Basic Type: 00:09:00</p></div><div><div><p>•Vector Part One: 00:20:00</p></div><div><div><p>•Vectors Part Two: 00:25:00</p></div><div><div><p>•Vectors - Missing Values: 00:16:00</p></div><div><div><p>•Vectors - Coercion: 00:14:00</p></div><div><div><p>•Vectors - Naming: 00:10:00</p></div><div><div><p>•Vectors - Misc: 00:06:00</p></div><div><div><p>•Creating Matrics: 00:31:00</p></div><div><div><p>•List: 00:32:00</p></div><div><div><p>•Introduction to Data Frames: 00:19:00</p></div><div><div><p>•Creating Data Frames: 00:20:00</p></div><div><div><p>•Data Frames: Helper Functions: 00:31:00</p></div><div><div><p>•Data Frames Tibbles: 00:39:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Intermediate Introduction: 00:47:00</p></div><div><div><p>•Relational Operations: 00:11:00</p></div><div><div><p>•Conditional Statements: 00:11:00</p></div><div><div><p>•Loops: 00:08:00</p></div><div><div><p>•Functions: 00:14:00</p></div><div><div><p>•Packages: 00:11:00</p></div><div><div><p>•Factors: 00:28:00</p></div><div><div><p>•Dates and Times: 00:30:00</p></div><div><div><p>•Functional Programming: 00:37:00</p></div><div><div><p>•Data Import or Export: 00:22:00</p></div><div><div><p>•Database: 00:27:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Manipulation in R Introduction: 00:36:00</p></div><div><div><p>•Tidy Data: 00:11:00</p></div><div><div><p>•The Pipe Operator: 00:15:00</p></div><div><div><p>•The Filter Verb: 00:22:00</p></div><div><div><p>•The Select Verb: 00:46:00</p></div><div><div><p>•The Mutate Verb: 00:32:00</p></div><div><div><p>•The Arrange Verb: 00:10:00</p></div><div><div><p>•The Summarize Verb: 00:23:00</p></div><div><div><p>•Data Pivoting: 00:43:00</p></div><div><div><p>•JSON Parsing: 00:11:00</p></div><div><div><p>•String Manipulation: 00:33:00</p></div><div><div><p>•Web Scraping: 00:59:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Visualization in R Section Intro: 00:17:00</p></div><div><div><p>•Getting Started: 00:16:00</p></div><div><div><p>•Aesthetics Mappings: 00:25:00</p></div><div><div><p>•Single Variable Plots: 00:37:00</p></div><div><div><p>•Two Variable Plots: 00:21:00</p></div><div><div><p>•Facets, Layering, and Coordinate Systems: 00:18:00</p></div><div><div><p>•Styling and Saving: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Creating with R Markdown: 00:29:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction to R Shiny: 00:26:00</p></div><div><div><p>•A Basic R Shiny App: 00:31:00</p></div><div><div><p>•Other Examples with R Shiny: 00:34:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Machine Learning Part 1: 00:22:00</p></div><div><div><p>•Machine Learning Part 2: 00:47:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Starting a Data Science Career Section Overview: 00:03:00</p></div><div><div><p>•Data Science Resume: 00:04:00</p></div><div><div><p>•Getting Started with Freelancing: 00:05:00</p></div><div><div><p>•Top Freelance Websites: 00:05:00</p></div><div><div><p>•Personal Branding: 00:05:00</p></div><div><div><p>•Importance of Website and Blo: 00:04:00</p></div><div><div><p>•Networking Do's and Don'ts: 00:04:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Apex Learning

Online Course Marketing

Online Course Creation

Data Engineering

Data Science

Data Skills

Trainers Course

Data Science & Machine Learning with R

<p><strong>***24 Hour Limited Time Flash Sale***<br /><br /></strong><strong> Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma Admission Gifts <br /><br /></strong><strong> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter</strong></p><p>Are you a professional feeling stuck in your career, struggling to keep up with the ever-changing demands of the industry? Or perhaps you're a beginner, unsure of where to start or how to break into your desired field. Whichever stage you're in, our exclusive Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma Bundle provides unique insights and tools that can help you achieve your goals.</p><p>Designed to cater to the needs of both seasoned professionals and aspiring newcomers, our Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma bundle is a comprehensive program that will equip you with the essential skills and knowledge you need to succeed. Whether you're looking to advance in your current role or embark on a new career journey, this bundle has everything you need to take your professional life to the next level.</p><p><strong>But that's not all. When you enrol in Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma Online Training, you'll receive 20 CPD-Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free.</strong></p><p><strong>Courses Are Included In this Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma Career Bundle:</strong></p><ul><li><strong>Course 01: </strong>Statistics for Data Science &amp; Machine Learning</li><li><strong>Course 02: </strong>Data Science &amp; Machine Learning with R Training</li><li><strong>Course 03: </strong>R Programming for Data Science</li><li><strong>Course 04: </strong>Statistical Concepts in R</li><li><strong>Course 05: </strong>Learn Python, JavaScript, and Microsoft SQL for Data Science</li><li><strong>Course 06: </strong>Data Science &amp; Machine Learning with Python</li><li><strong>Course 07: </strong>Spatial Data Visualization and Machine Learning in Python</li><li><strong>Course 08: </strong>Azure Machine Learning</li><li><strong>Course 09: </strong>Python Data Science</li><li><strong>Course 10: </strong>Python Data Science with Numpy, Pandas, and Matplotlib</li><li><strong>Course 11: </strong>Quick Data Science Approach from Scratch</li><li><strong>Course 12: </strong>Learn MySQL from Scratch for Data Science and Analytics</li><li><strong>Course 13: </strong>Hands-on Machine Learning Project - Auto Image Captioning for Social Media</li><li><strong>Course 14: </strong>Statistical Analysis</li><li><strong>Course 15: </strong>Statistics Essentials</li><li><strong>Course 16: </strong>Data Analysis In Excel</li><li><strong>Course 17: </strong>Microsoft Excel Course for Everyone - Complete the Excel Course</li><li><strong>Course 18: </strong>Introduction to Discrete Maths</li><li><strong>Course 19: </strong>Advanced Mathematics Training<strong><br /></strong></li><li><strong>Course 20: </strong>Computer Simulation of Realistic Mathematical Models</li></ul><p>With Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma, you'll embark on an immersive learning experience that combines interactive lessons with voice-over audio, ensuring that you can learn from anywhere in the world, at your own pace. With 24/7 tutor support, you'll never feel alone in your journey, whether you're a seasoned professional or a beginner.</p><p>Don't let this opportunity pass you by. Enrol in Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma today and take the first step towards achieving your goals and dreams.</p><p><strong>Why buy this Advanced Diploma?</strong></p><ul><li><strong>Free CPD Accredited Certificate</strong> upon completion of Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma</li><li>Get <strong>a free student ID card</strong> with Statistics for this Course.</li><li><strong>Lifetime access</strong> to the course materials</li><li>Get instant access to this course</li><li>Learn Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma from anywhere in the world</li><li>24/7 tutor support with the Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma course.</li></ul> <p><strong>Start your learning journey straightaway with our Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma Training!</strong></p><p>Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma premium bundle consists of 20 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience.</p><p><strong>Certification</strong></p><p>You have to complete the assignment given at the end of the Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma course. After passing the Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma exam You will be entitled to claim a PDF &amp; Hardcopy certificate accredited by CPD Quality standards completely free.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>This course is ideal for:</p><ul><li>Students seeking mastery in this field</li><li>Professionals seeking to enhance their skills</li><li>Anyone who is passionate about this topic</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This <strong>Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma</strong> doesn't require prior experience and is suitable for diverse learners.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This <strong>Statistics for Data Science &amp; Machine Learning Level 7 Advanced Diploma</strong> bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>If you are an <strong>international student</strong>, then you have to pay an additional <strong>10 GBP</strong> for each certificate as an international delivery charge.</p></div>

                        </div>

Diploma in Professional Studies (DPS)

Statistics for Data Science & Machine Learning Level 7 Advanced Diploma - CPD Certified

<div>
	<h3><b>Course Overview</b></h3>
<p><span>Peek into the world of data science and machine learning with the comprehensive <strong>Data Science &amp; Machine Learning With R in 2021</strong> course. This course will provide you with a detailed understanding of both machine learning and data science. In addition, you will acquire essential skills to pursue a career in this growing industry.</span></p>
<p><span>The <strong>Data Science &amp; Machine Learning With R in 2021</strong> course will teach you the core concept of data science. You will be able to recognize different data types and structures. From the modules, you will receive an introduction to the intermediate R Section. The course will show you the techniques of data manipulation in R. You will know the process of data visualization with R and learn to create reports with R markdown. The Data Science &amp; Machine Learning With R in 2021 course will provide you with an insight into the fundamentals of machine learning. You will understand the principles of data processing, linear regression, logistic regression and more. </span></p>
<p><span>This highly informative <strong>Data Science &amp; Machine Learning With R in 2021</strong> course will equip you with the essential skills of data science. If you desire to become a professional data scientist, this course can be your stepping stone. So, enroll in the course and fast track your career.</span></p>
<h3><b>Learning Outcomes</b></h3>
<ul>
<li><span>Learn the definition of data science </span></li>
<li><span>Understand the basics of machine learning</span></li>
<li><span>Enrich your knowledge of data types and structures</span></li>
<li><span>Know the process of data manipulation in R</span></li>
<li><span>Gain the ability to create reports with R markdown</span></li>
<li><span>Become skilled in building web apps with R shiny</span></li>
</ul>
<h3><b>Who is this course for?</b></h3>
<p><span>Aspiring data scientists or individuals interested in learning data science and machine learning </span></p>
<h3><b>Entry Requirement</b></h3>
<ul>
<li><span>This course is available to all learners, of all academic backgrounds.</span></li>
<li><span>Learners should be aged 16 or over to undertake the qualification.</span></li>
<li><span>Good understanding of English language, numeracy and ICT are required to attend this course.</span></li>
</ul>
<h3><b>Certification</b></h3>
<ul>
<li><span>After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £39 or in PDF format at the cost of £24.</span></li>
<li><span>PDF certificate's turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days.</span></li>
</ul>
<h3><b>Why choose us?</b></h3>
<ul>
<li><span>Affordable, engaging &amp; high-quality e-learning study materials;</span></li>
<li><span>Tutorial videos/materials from the industry-leading experts;</span></li>
<li><span>Study in a user-friendly, advanced online learning platform;</span></li>
<li><span>Efficient exam systems for the assessment and instant result;</span></li>
<li><span>The UK &amp; internationally recognized accredited qualification;</span></li>
<li><span>Access to course content on mobile, tablet or desktop from anywhere anytime;</span></li>
<li><span>The benefit of career advancement opportunities;</span></li>
<li><span>24/7 student support via email.</span></li>
</ul>
<h3><b>Career Path</b></h3>
<p><b> </b><span>The <strong>Data Science &amp; Machine Learning With R in 2021</strong></span><b>  </b><span>course is a useful qualification to possess and would be beneficial for any related profession or industry such as:</span></p>
<ul>
<li><span>Data Scientist </span></li>
</ul>
		</div><table width="100%">				<tbody><tr>
					<td colspan="4">Data Science and Machine Learning Course Intro</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Introduction</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>What is Data Science</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Overview</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Who is This Course for</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Marketplace</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Job Opportunities</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Types and Structures in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Basics</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Files</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>RStudio</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tidyverse</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Resources</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Types and Structures in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Unit Introduction</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Basic Type</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vector Part One</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors Part Two</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Missing Values</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Coercion</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Naming</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Misc</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Matrics</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>List</td>
					<td> </td>
					<td><span><i></i> 00:32:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Data Frames</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Data Frames</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Frames: Helper Functions</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Frames Tibbles</td>
					<td> </td>
					<td><span><i></i> 00:39:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Intermediate R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Intermediate Introduction</td>
					<td> </td>
					<td><span><i></i> 00:47:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Relational Operations</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Conditional Statements</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Loops</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Functions</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Packages</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Factors</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Dates and Times</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Functional Programming</td>
					<td> </td>
					<td><span><i></i> 00:37:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Import or Export</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Database1</td>
					<td> </td>
					<td><span><i></i> 00:27:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Manipulation in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Manipulation in R Introduction</td>
					<td> </td>
					<td><span><i></i> 00:36:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tidy Data</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Pipe Operator</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Filter Verb</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Select Verb</td>
					<td> </td>
					<td><span><i></i> 00:46:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Mutate Verb</td>
					<td> </td>
					<td><span><i></i> 00:32:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Arrange Verb</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Summarize Verb</td>
					<td> </td>
					<td><span><i></i> 00:23:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Pivoting</td>
					<td> </td>
					<td><span><i></i> 00:43:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>JSON Parsing</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>String Manipulation</td>
					<td> </td>
					<td><span><i></i> 00:33:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Web Scraping</td>
					<td> </td>
					<td><span><i></i> 00:59:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Visualization in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Visualization in R Section Intro</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Aesthetics Mappings</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Single Variable Plots</td>
					<td> </td>
					<td><span><i></i> 00:37:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Two Variable Plots</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Facets, Layering, and Coordinate Systems</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Styling and Saving</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Creating Reports with R Markdown</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating with R Markdown</td>
					<td> </td>
					<td><span><i></i> 00:29:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Building Webapps with R Shiny</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to R Shiny</td>
					<td> </td>
					<td><span><i></i> 00:26:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>A Basic R Shiny App</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Other Examples with R Shiny</td>
					<td> </td>
					<td><span><i></i> 00:34:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Introduction to Machine Learning</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Part 1</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Part 2</td>
					<td> </td>
					<td><span><i></i> 00:47:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Starting A Career in Data Science</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Starting a Data Science Career Section Overview</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science Resume</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started with Freelancing</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Top Freelance Websites</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Personal Branding</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Importance of Website and Blo</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Networking Do's and Don'ts</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Certificate and Transcript</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Order Your Certificates and Transcripts</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table>

John Academy

Data Science & Machine Learning With R in 2021

<p>Get ready for an exceptional online learning experience with the <strong>Python, Data Science, Machine Learning, SQL, Cloud computing &amp; Cyber Security </strong>bundle! This carefully curated collection of<strong> 20 premium courses</strong> is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations.</p><p>The Python &amp; Data Science package is dynamic, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you're looking to break into a new field or deepen your existing knowledge, the Python, Data Science, Machine Learning, SQL, Cloud computing &amp; Cyber Securitypackage has something for everyone.</p><p>As part of the Python &amp; Data Science package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Python &amp; Data Science bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth!</p><p><strong>This Bundle Comprises the Following </strong><strong>Python, Data Science, Machine Learning, SQL, Cloud computing &amp; Cyber Security  </strong><strong>CPD-accredited courses:</strong></p><ul><li><strong>Course 01: </strong>Python Programming: Beginner To Expert</li><li><strong>Course 02:</strong> Data Science &amp; Machine Learning with Python</li><li><strong>Course 03: </strong>Coding with Python 3</li><li><strong>Course 04: </strong>Introduction to Coding With HTML, CSS, &amp; Javascript</li><li><strong>Course 05: </strong>Python for Spatial Analysis in ArcGIS</li><li><strong>Course 06: </strong>Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li><strong>Course 07: </strong>Business Intelligence and Data Mining</li><li><strong>Course 08: </strong>SQL for Data Science, Data Analytics and Data Visualization</li><li><strong>Course 09: </strong>Python Data Science with Numpy, Pandas and Matplotlib</li><li><strong>Course 10: </strong>Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li><strong>Course 11: </strong>Cyber Security Awareness Training</li><li><strong>Course 12:</strong> Learn Ethical Hacking From A-Z: Beginner To Expert</li><li><strong>Course 13: </strong>Easy to Advanced Data Structures</li><li><strong>Course 14: </strong>R Programming for Data Science</li><li><strong>Course 15: </strong>GDPR UK Training</li><li><strong>Course 16: </strong>Career Development Plan Fundamentals</li><li><strong>Course 17: </strong>CV Writing and Job Searching</li><li><strong>Course 18: </strong>Learn to Level Up Your Leadership</li><li><strong>Course 19: </strong>Networking Skills for Personal Success</li><li><strong>Course 20: </strong>Ace Your Presentations: Public Speaking Masterclass</li></ul><p><strong>What will make you stand out?</strong></p><p>Upon completion of this online Python, Data Science, Machine Learning, SQL, Cloud computing &amp; Cyber Security bundle, you will gain the following:</p><ul><li>CPD QS Accredited Proficiency with this Python &amp; Data Science bundle</li><li>After successfully completing the Python &amp; Data Science bundle, you will receive a FREE PDF Certificate from REED as evidence of your newly acquired abilities.</li><li>Lifetime access to the whole collection of learning materials of this Python &amp; Data Science bundle</li><li>The online test with immediate results</li><li>You can study and complete the Python &amp; Data Science bundle at your own pace.</li><li>Study for the Python &amp; Data Science bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p>Each course in this Python, Data Science, Machine Learning, SQL, Cloud computing &amp; Cyber Security bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This Python &amp; Data Science bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development.</p><p>Embrace the future of learning with the Python, Data Science, Machine Learning, SQL, Cloud computing &amp; Cyber Security , a rich anthology of 30 diverse courses. Our experts handpick each course in the Python &amp; Data Science bundle to ensure a wide spectrum of learning opportunities. This Python &amp; Data Science bundle will take you on a unique and enriching educational journey.</p><p>The Python &amp; Data Science bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Python &amp; Data Science bundle offers you the flexibility and convenience to learn at your own pace. Make the Python &amp; Data Science package your trusted companion in your lifelong learning journey.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>The <strong>Python, Data Science, Machine Learning, SQL, Cloud computing &amp; Cyber Security  </strong>bundle is perfect for:</p><ul><li><strong>Beginners interested in technology: </strong>Individuals with little to no background in tech who are looking to start their careers in various IT domains.</li><li><strong>Aspiring Data Scientists and Analysts:</strong> Those aiming to build foundational and advanced skills in data science and machine learning.</li><li><strong>Future Software Developers: </strong>Individuals interested in learning Python and SQL for software development or database management.</li><li><strong>IT Security Enthusiasts: </strong>People who are keen on entering the field of cyber security and want to understand how to protect data in a digital environment.</li><li><strong>Cloud Computing Aspirants:</strong> Those looking to gain skills in cloud technologies and understand how to manage and deploy applications on the cloud.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You are warmly invited to register for this Python, Data Science, Machine Learning, SQL, Cloud computing &amp; Cyber Security bundle. Please be aware that no formal entry requirements or qualifications are necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Upon <strong>Python, Data Science, Machine Learning, SQL, Cloud computing &amp; Cyber Security  </strong>course completion, you can expect to:</p><ul><li>Python Developer</li><li>Data Analyst</li><li>Machine Learning Engineer</li><li>Database Administrator</li><li>Cloud Solutions Architect</li><li>Cyber Security Specialist</li><li>Data Science Consultant</li><li>Systems Analyst</li><li>Network Security Engineer</li><li>Research Scientist (AI/ML)</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>20 CPD Quality Standard Pdf Certificates</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Compliance Central

Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security

<p>Learn the logical concepts of Data Science &amp; Machine Learning with R, a comprehensive course that covers key topics such as data manipulation, visualisation, report creation, web app development, and introduction to machine learning. Gain the necessary skills to start a successful career in the field of Data Science and Machine Learning.</p><p><strong>Key Takeaways:</strong></p><ul><li>Understand the fundamentals of Data Science and Machine Learning using R.</li><li>Analyse and manipulate data using various data types and structures in R.</li><li>Create visually appealing data visualisations to extract insights from complex datasets.</li><li>Develop interactive web applications using R Shiny for data presentation and analysis.</li><li>Gain proficiency in creating reports using R Markdown for efficient data communication.</li><li>Explore the basics of Machine Learning and its applications in solving real-world problems.</li><li>Acquire the knowledge and confidence to pursue a rewarding career in Data Science.</li></ul><p><strong>Our Service for the </strong><strong>Data Science &amp; Machine Learning with R</strong><strong> Includes: </strong></p><ul><li>Intensive Study Notes</li><li>Tutor Support</li><li>Customer Support</li><li>Assignment Help</li><li>QLS Endorsed Certificate</li><li>24/7 Learning Portal Access</li><li>Widely Compatible Study Materials</li></ul><p><strong>Course Curriculum of </strong><strong>Data Science &amp; Machine Learning with R</strong></p><ul><li>Data Science and Machine Learning Course Intro</li><li>Getting Started with R</li><li>Data Types and Structures in R</li><li>Intermediate R</li><li>Data Manipulation in R</li><li>Data Visualization in R</li><li>Creating Reports with R Markdown</li><li>Building Webapps with R Shiny</li><li>Introduction to Machine Learning</li><li>Starting A Career in Data Science</li></ul><p><strong>Assessment &amp; Certification</strong></p><p>Learners must submit a comprehensive summerised assignment on all the units that will be assessed by our expert tutor if all the learning outcomes are met as per the standard set by QLS. After the quality check, learner will be provided with the Data Science &amp; Machine Learning with R certificate.</p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme</strong></p><p>An endorsed certificate will be issued to learners at the end of the course as recognition of course completion. Provided that learner completes all the assessment of a course can claim for certificate.</p><p><strong>Endorsement:</strong></p><p>QLS endorsed this certified course as a highly qualified, non-regulated provision and training programme. There will be a trainer to answer all your questions including your progression routes into further higher education.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Individuals interested in entering the field of Data Science and Machine Learning.</li><li>Professionals looking to enhance their data manipulation and analysis skills using R.</li><li>Graduates seeking to gain a competitive edge in the job market with specialized knowledge in Data Science.</li><li>Those who want to explore the applications of Machine Learning and its impact on various industries.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal requirements needed for this Data Science &amp; Machine Learning with R course. So learners do not require any prior qualifications to enrol in this course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £25,000 - £45,000 per annum</li><li>Machine Learning Engineer: £35,000 - £65,000 per annum</li><li>Data Scientist: £40,000 - £70,000 per annum</li><li>Business Intelligence Analyst: £30,000 - £55,000 per annum</li><li>Data Engineer: £35,000 - £60,000 per annum</li><li>AI Research Scientist: £50,000 - £90,000 per annum</li></ul>
                        </div>

Kingston Open College

Learning Support Assistant (LSA)

Level 7 QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

Level 7 Diploma in Data Science & Machine Learning with R - QLS Endorsed

<strong>In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.</strong>
        <p>Recurrent Neural Networks are a type of deep learning architecture designed to process sequential data, such as time series, text, speech, and video. RNNs have a memory mechanism, which allows them to preserve information from past inputs and use it to inform their predictions.

TensorFlow 2 is a popular open-source software library for machine learning and deep learning. It provides a high-level API for building and training machine learning models, including RNNs.

In this compact course, you will learn how to use TensorFlow 2 to build RNNs. We will study the Simple RNN (Elman unit), the GRU, and the LSTM, followed by investigating the capabilities of the different RNN units in terms of their ability to detect nonlinear relationships and long-term dependencies. We will apply RNNs to both time series forecasting and NLP. Next, we will apply LSTMs to stock 'price' predictions, but in a different way compared to most other resources. It will mostly be an investigation about what not to do and how not to make the same mistakes that most blogs and courses make when predicting stocks.

By the end of this course, you will be able to build your own build RNNs with TensorFlow 2.</p>
        <h2>What You Will Learn</h2><p>Learn about simple RNNs (Elman unit)<br>Covers GRU (gated recurrent unit)<br>Learn how to use LSTM (long short-term memory unit)<br>Learn how to preform time series forecasting<br>Learn how to predict stock price and stock return with LSTM<br>Learn how to apply RNNs to NLP</p>
        <h2>Audience</h2><p>This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement recurrent neural networks in TensorFlow 2. One must have decent Python programming skills, should know how to build a feedforward ANN in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.</p>
        <h2>Approach</h2><p>In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks. The course is well-balanced with theory that explains the RNN concepts and hands-on coding exercises for practical understanding. The course includes video presentations, coding lessons, hands-on exercises, and links to further resources.</p>
        <h2>Key Features</h2><p>Build your own RNNs with TensorFlow 2 * Explains RNNs, time series, and sequence data * Preform text preprocessing and text classification with LSTMs</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will get introduced to recurrent neural networks and understand the course learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outline</p><p style='margin: 0'>In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.</p></td></tr></tbody></table><br><h4>2. Recurrent Neural Networks (RNNs), Time Series, and Sequence Data</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Sequence Data</p><p style='margin: 0'>In this video, you will learn about sequence data.</p></td></tr><tr><td style='padding: 15px;'><p>2. Forecasting</p><p style='margin: 0'>In this video, we will discuss about forecasting.</p></td></tr><tr><td style='padding: 15px;'><p>3. Autoregressive Linear Model for Time Series Prediction</p><p style='margin: 0'>In this video, we will dive into coding and learn about the autoregressive linear model for time series prediction.</p></td></tr><tr><td style='padding: 15px;'><p>4. Proof That the Linear Model Works</p><p style='margin: 0'>In this video, you will learn how the linear model is about to forecast for a sine wave.</p></td></tr><tr><td style='padding: 15px;'><p>5. Recurrent Neural Networks (Elman Unit Part 1)</p><p style='margin: 0'>In this video, we will get introduced to simple recurrent neural networks also called as Elman Unit.</p></td></tr><tr><td style='padding: 15px;'><p>6. Recurrent Neural Networks (Elman Unit Part 2)</p><p style='margin: 0'>In this video, you will learn how to use the simple RNN to solve many-to-one tasks and many-to-many problems.</p></td></tr><tr><td style='padding: 15px;'><p>7. RNN Code Preparation</p><p style='margin: 0'>In this video, we will understand how to write code in TensorFlow 2 for a simple RNN.</p></td></tr><tr><td style='padding: 15px;'><p>8. RNN for Time Series Prediction</p><p style='margin: 0'>In this video, we will do the same forecasting exercise that we did with an autoregressive liner model, but with a simple RNN instead.</p></td></tr><tr><td style='padding: 15px;'><p>9. Paying Attention to Shapes</p><p style='margin: 0'>In this video, we will work on shapes in RNN.</p></td></tr><tr><td style='padding: 15px;'><p>10. GRU and LSTM (Part 1)</p><p style='margin: 0'>In this video, you will learn about modern RNN units called GRU.</p></td></tr><tr><td style='padding: 15px;'><p>11. GRU and LSTM (Part 2)</p><p style='margin: 0'>In this video, you will learn about modern RNN units called LSTM.</p></td></tr><tr><td style='padding: 15px;'><p>12. A More Challenging Sequence</p><p style='margin: 0'>In this video, you will learn how to preform time series prediction with a complex signal.</p></td></tr><tr><td style='padding: 15px;'><p>13. Demo of the Long-Distance Problem</p><p style='margin: 0'>In this video, we will understand how LSTMs work for resolving a long-distance problem.</p></td></tr><tr><td style='padding: 15px;'><p>14. RNN for Image Classification (Theory)</p><p style='margin: 0'>In this video, we will understand how to apply RNN for image classification.</p></td></tr><tr><td style='padding: 15px;'><p>15. RNN for Image Classification (Code)</p><p style='margin: 0'>In this video, you will learn how to preform RNN for image classification on the MNIST dataset.</p></td></tr><tr><td style='padding: 15px;'><p>16. Stock Return Predictions Using LSTMs (Part 1)</p><p style='margin: 0'>In this video, we will understand why one-step prediction on stock price is misleading and unconventional.</p></td></tr><tr><td style='padding: 15px;'><p>17. Stock Return Predictions Using LSTMs (Part 2)</p><p style='margin: 0'>In this video, we will work on a more conventional prediction called stock return.</p></td></tr><tr><td style='padding: 15px;'><p>18. Stock Return Predictions Using LSTMs (Part 3)</p><p style='margin: 0'>In this video, we will make use of all the data and try to predict whether the price will go up or down.</p></td></tr><tr><td style='padding: 15px;'><p>19. Other Ways to Forecast</p><p style='margin: 0'>In this video, we will discuss some other ways that might generate multi-step forecast.</p></td></tr><tr><td style='padding: 15px;'><p>20. Suggestion Box</p><p style='margin: 0'>In this video, the author shares how you can get in touch with him through the suggestion box.</p></td></tr></tbody></table><br><h4>3. Natural Language Processing (NLP)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Embeddings</p><p style='margin: 0'>In this video, we will understand what embedding is.</p></td></tr><tr><td style='padding: 15px;'><p>2. Code Preparation (NLP)</p><p style='margin: 0'>In this video, you will learn how to write the code to turn a sequence of words into an acceptable format such that they can be converted into a matrix of numbers.</p></td></tr><tr><td style='padding: 15px;'><p>3. Text Preprocessing</p><p style='margin: 0'>In this video, we will work on text preprocessing.</p></td></tr><tr><td style='padding: 15px;'><p>4. Text Classification with LSTMs</p><p style='margin: 0'>In this video, we will work on text classification with LSTMs.</p></td></tr></tbody></table>

Packt

Python

TensorFlow

In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.

Deep Learning - Recurrent Neural Networks with TensorFlow

<strong>If you are working on data science projects and want to create powerful visualization and insights as an outcome of your projects or are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course exclusively focuses on explaining how to build fantastic visualizations using Python. It covers more than 20 types of visualizations using the most popular Python visualization libraries, such as Matplotlib, Seaborn, and Bokeh along with data analytics that leads to building these visualizations so that the learners understand the flow of analysis to insights.</strong>
        <p>If you are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course takes a holistic approach to teach visualization techniques.

We will be taking real-life business scenarios and raw data to go through detailed Exploratory Data Analysis (EDA) techniques to prepare the raw data to suit the appropriate visualization needs. You will learn about data analytics and exploratory data analysis techniques using multiple different data structures with NumPy and Pandas libraries. You will also learn various chart/graph types, customization/configuration, and vectorization techniques.

We will look at advanced visualizations using business applications such as single and multiple bar charts, pie charts, and bubble charts with the vectorization of properties. We will further explore Seaborn Boxplot, Violin plot, Categorical Scatterplot, and how to create heat maps.

By the end of the course, you will learn the foundational techniques of data analytics and deeper customizations on visualizations. You will be able to confidently use Python visualization libraries such as Matplotlib, Seaborn, and Bokeh in your future projects.

All resources and code files are placed here: https://github.com/PacktPublishing/Data-Analytics-using-Python-Visualizations
</p>
        <h2>What You Will Learn</h2><p>Learn about the various visualization concepts<br>Learn to create simple plots using Matplotlib<br>Learn about marginal histograms and marginal boxplots<br>Learn handling images using pixel metrics<br>Learn about categorical variables and histograms (with EDA)<br>Learn various data generation techniques</p>
        <h2>Audience</h2><p>This course is for Python and machine learning developers, data scientists, data analysts, and business analysts. This course will also be beneficial to leaders, managers, and anyone whose job involves presenting data in the form of visuals, which include developers, architects, and system analysts.<br><br>A basic understanding of Python will be helpful, but not mandatory.</p>
        <h2>Approach</h2><p>This practical hands-on course has easy, step-by-step explanations with code to draw over 20 diverse kinds of charts and graphs using Python. Extensive quizzes are infused at logical points to validate the learning effectiveness.</p>
        <h2>Key Features</h2><p>The art of presenting data in the form of powerful, innovative, and intuitive visualizations * In-depth coverage of Matplotlib, Seaborn, and Bokeh visualization libraries * Use of data analytics techniques/Exploratory Data Analysis (EDA) using several data generations and manipulation methods</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Data-Analytics-using-Python-Visualizations</p>
        <h2>About the Author</h2><h3>Manas Dasgupta</h3><p>Manas Dasgupta holds a master's degree (MSc) from the Liverpool John Moore's University (LJMU), the UK in Artificial Intelligence and Machine Learning (AI/ML). My specialization and research areas are Natural Language Processing (NLP) using Deep Learning Methods such as Siamese Networks, Encoder-Decoder techniques, various Language Embedding methods such as BERT, and areas such as Supervised Learning on Semantic Similarity and so on.

His expertise area also encompasses an array of Machine Learning and Data Science / Predictive Analytics areas including various Supervised, Unsupervised, and Clustering methods.

He has almost 20 Years of experience in the IT Industry, mostly in the Financial Services domain. Starting as a Developer to being an Architect for several years to a leadership position. His key focus and passion are to increase technical breadth and innovation.</p>
        <h2>Course Outline</h2><h4>1. Matplotlib and Seaborn - Libraries and Techniques</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Promotional Video</p><p style='margin: 0'>This video explains the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Author Introduction</p><p style='margin: 0'>This video introduces you to the author.</p></td></tr><tr><td style='padding: 15px;'><p>3. What You Will Learn</p><p style='margin: 0'>This video explains what the course outcomes are.</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualization Concepts</p><p style='margin: 0'>This video explains visualization concepts.</p></td></tr><tr><td style='padding: 15px;'><p>5. Introduction to Matplotlib</p><p style='margin: 0'>This video explains Matplotlib.</p></td></tr><tr><td style='padding: 15px;'><p>6. Creating Simple Plots Using Matplotlib</p><p style='margin: 0'>This video explains how to create simple plots using Matplotlib.</p></td></tr><tr><td style='padding: 15px;'><p>7. Creating Scatter Plots</p><p style='margin: 0'>This video explains creating scatter plots.</p></td></tr><tr><td style='padding: 15px;'><p>8. Creating Axis Limits</p><p style='margin: 0'>This video explains how to create axis limits.</p></td></tr><tr><td style='padding: 15px;'><p>9. Parameterizing Plots</p><p style='margin: 0'>This video explains parameterizing plots.</p></td></tr><tr><td style='padding: 15px;'><p>10. Creating Error Bars</p><p style='margin: 0'>This video explains creating error bars.</p></td></tr><tr><td style='padding: 15px;'><p>11. Plotting Histograms and Box Plots</p><p style='margin: 0'>This video explains plotting histograms and box plots.</p></td></tr><tr><td style='padding: 15px;'><p>12. Plotting 2D Histograms</p><p style='margin: 0'>This video explains plotting 2D histograms.</p></td></tr><tr><td style='padding: 15px;'><p>13. Marginal Histograms and Marginal Boxplots</p><p style='margin: 0'>This video explains marginal histograms and marginal boxplots.</p></td></tr><tr><td style='padding: 15px;'><p>14. Working with Subplots</p><p style='margin: 0'>This video explains working with subplots.</p></td></tr><tr><td style='padding: 15px;'><p>15. Stock Trend / Time Series Plot and Annotations</p><p style='margin: 0'>This video explains stock trend / time series plot and annotations.</p></td></tr><tr><td style='padding: 15px;'><p>16. Plotting Images and Clustering</p><p style='margin: 0'>This video explains plotting images and clustering.</p></td></tr><tr><td style='padding: 15px;'><p>17. Creating 2D Contour plots for 3D Data</p><p style='margin: 0'>This video explains creating 2D contour plots for 3D data.</p></td></tr><tr><td style='padding: 15px;'><p>18. Creating 3D Plots Including 3D Contours</p><p style='margin: 0'>This video explains creating 3D plots including 3D contours.</p></td></tr><tr><td style='padding: 15px;'><p>19. Stylesheets, rcParam, and Custom Stylesheets</p><p style='margin: 0'>This video explains stylesheets, rcParam, and custom stylesheets.</p></td></tr></tbody></table><br><h4>2. Advanced Visualizations Using Business Applications</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Single and Multiple Bar Charts</p><p style='margin: 0'>This video explains single and multiple bar charts.</p></td></tr><tr><td style='padding: 15px;'><p>2. Area and Stacked-Area Charts</p><p style='margin: 0'>This video explains area and stacked-area charts.</p></td></tr><tr><td style='padding: 15px;'><p>3. Drawing Pie Charts</p><p style='margin: 0'>This video explains drawing pie charts.</p></td></tr><tr><td style='padding: 15px;'><p>4. Bubble Charts with Vectorization of Properties</p><p style='margin: 0'>This video explains bubble charts with vectorization of properties.</p></td></tr><tr><td style='padding: 15px;'><p>5. Plotting Regression Lines with OLS (ML)</p><p style='margin: 0'>This video explains plotting regression lines with OLS (ML).</p></td></tr><tr><td style='padding: 15px;'><p>6. Categorical Variables and Histograms (with EDA)</p><p style='margin: 0'>This video explains categorical variables and histograms.</p></td></tr><tr><td style='padding: 15px;'><p>7. Seaborn Boxplot, Violin plot, Categorical Scatterplot</p><p style='margin: 0'>This video explains Seaborn Boxplot, Violin plot, and Categorical Scatterplot.</p></td></tr><tr><td style='padding: 15px;'><p>8. Seaborn Slopeplots for Comparing Distributions</p><p style='margin: 0'>This video explains Seaborn Slopeplots for comparing distributions.</p></td></tr><tr><td style='padding: 15px;'><p>9. Dumbbell Plot for Category-Wise Value Movement</p><p style='margin: 0'>This video explains Dumbbell plot for category-wise value movement.</p></td></tr><tr><td style='padding: 15px;'><p>10. Creating Heatmaps</p><p style='margin: 0'>This video explains creating heatmaps.</p></td></tr><tr><td style='padding: 15px;'><p>11. Working with Pairplots</p><p style='margin: 0'>This video explains working with Pairplots.</p></td></tr><tr><td style='padding: 15px;'><p>12. Seasonal Trendcharts</p><p style='margin: 0'>This video explains seasonal Trendcharts.</p></td></tr><tr><td style='padding: 15px;'><p>13. Yearplot and Calendarplot for Color-Scaled Trends</p><p style='margin: 0'>This video explains Yearplot and Calendarplot for color-scaled trends.</p></td></tr><tr><td style='padding: 15px;'><p>14. Radarplot to Compare Scores of Multiple Parameters</p><p style='margin: 0'>This video explains Radarplot to compare scores of multiple parameters.</p></td></tr></tbody></table><br><h4>3. Working with the Beautiful and Powerful Bokeh Library</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Bokeh</p><p style='margin: 0'>This video introduces you to Bokeh.</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating Simple and Multiple Line Plots</p><p style='margin: 0'>This video explains how to create simple and multiple line plots.</p></td></tr><tr><td style='padding: 15px;'><p>3. Customizing Your Plots</p><p style='margin: 0'>This video explains how to customize your plots.</p></td></tr><tr><td style='padding: 15px;'><p>4. Creating Bubble Plots - Vectorizing Your Plot</p><p style='margin: 0'>This video explains how to create bubble plots - vectorizing your plot.</p></td></tr><tr><td style='padding: 15px;'><p>5. Working with Layouts - Row / Column / Grid</p><p style='margin: 0'>This video explains working with layouts - row / column / grid.</p></td></tr><tr><td style='padding: 15px;'><p>6. Using the ColumnDataSource Object</p><p style='margin: 0'>This video explains using the ColumnDataSource object.</p></td></tr><tr><td style='padding: 15px;'><p>7. Applying Filters - IndexFilter, BooleanFilter, GroupFilter</p><p style='margin: 0'>This video explains applying filters - IndexFilter, BooleanFilter, GroupFilter.</p></td></tr><tr><td style='padding: 15px;'><p>8. Widgets - Dynamic Plot Controls</p><p style='margin: 0'>This video explains widgets - dynamic plot controls.</p></td></tr><tr><td style='padding: 15px;'><p>9. Plotting on a Google Map Using Google Map API</p><p style='margin: 0'>This video explains plotting on a Google Map using Google Map API.</p></td></tr><tr><td style='padding: 15px;'><p>10. Closing Notes</p><p style='margin: 0'>This video wraps up the course.</p></td></tr></tbody></table>

Analytics

Data Visualization

If you are working on data science projects and want to create powerful visualization and insights as an outcome of your projects or are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course exclusively focuses on explaining how to build fantastic visualizations using Python. It covers more than 20 types of visualizations using the most popular Python visualization libraries, such as Matplotlib, Seaborn, and Bokeh along with data analytics that leads to building these visualizations so that the learners understand the flow of analysis to insights.

Data Analytics Using Python Visualizations

<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

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 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>

Deep Learning

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

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