Name: The Complete Self-Driving Car Course - Applied Deep Learning
Price: 29.99 GBP
Availability: InStock

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Explore the diverse realm of Big Data Analytics, delving into data mining, machine learning, and natural language processing. Uncover the ethical and legal dimensions, and gain practical insights through real-world case studies.
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<p>Discover types of Big Data analytics, delve into the data mining process, and apply machine learning techniques. Learn about natural language processing principles, assess the impact across industries, and navigate ethical considerations.<br /><br /></p>
<p>After the successful completion of this course, you will be able to:<br /><br /></p>
<ul>
<li>Identify the types of Big Data analytics, such as descriptive, predictive, and prescriptive analytics.</li>
<li>Understand the data mining process and its role in Big Data analytics.</li>
<li>Apply machine learning techniques like classification, regression, and clustering to analyse Big Data.</li>
<li>Describe the principles and applications of natural language processing in Big Data analytics.</li>
<li>Recognize the benefits and limitations of Big Data analytics in different industries, such as healthcare, finance, and marketing.</li>
<li>Evaluate the accuracy and reliability of Big Data analytics results.</li>
<li>Understand Big Data analytics's ethical and legal considerations, such as data privacy and intellectual property rights.</li>
<li>Analyze case studies and examples of successful implementation of Big Data analytics in different industries, such as predicting customer churn, fraud detection, and personalized medicine.</li>
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<div> <div id="headingTwo"> <h2>  </h2> </div> <div id="collapseTwo"> <div> <div> <div> <div> <div> <div id="msbmAccordion"> <div> <div> <div> <div> <div id="headingOne1"> <h2>  </h2> </div> <div id="collapse2898"> <div> <p>Immerse yourself in Big Data Analytics, mastering techniques to extract valuable insights. From predictive modelling to ethical considerations, this course empowers you with the skills to excel in a data-driven landscape, with applications ranging from healthcare to finance.</p> <ul> <li> <div> <div> <i></i> </div> <div> <h5>VIDEO - Course Structure and Assessment Guidelines</h5> <p>Watch this video to gain further insight.</p> </div> </div> </li> <li> <div> <div> <i></i> </div> <div> <h5>Navigating the MSBM Study Portal</h5> <p>Watch this video to gain further insight.</p> </div> </div> </li> <li> <div> <div> <i></i> </div> <div> <h5>Interacting with Lectures/Learning Components</h5> <p>Watch this video to gain further insight.</p> </div> </div> </li> <li> <div> <div> <i></i> </div> <div> <h5>Big Data Analytics, Techniques and Models</h5> <p>Self-paced pre-recorded learning content on this topic.</p> </div> </div> </li> <li> <div> <div> <i></i> </div> <div> <h5>Big Data Analytics</h5> <p>Put your knowledge to the test with this quiz. Read each question carefully and choose the response that you feel is correct. </p> </div> </div> </li> </ul> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
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All MSBM courses are accredited by the relevant partners and awarding bodies. Please refer to MSBM accreditation in about us for more details.
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There are no strict entry requirements for this course. Work experience will be an added advantage to understanding the content of the course.<br />
<p>The certificate is designed to enhance the learner's knowledge in the field. This certificate is for everyone who is eager to know more and get updated on current ideas in their respective field. We recommend this certificate for the following audience.</p>
<ul>
<li>Big Data Analyst</li>
<li>Data Scientist</li>
<li>Machine Learning Engineer</li>
<li>Business Intelligence Analyst</li>
<li>Predictive Analytics Expert</li>
<li>Information Architect</li>
<li>Data Engineer</li>
<li>AI Solutions Architect</li>
</ul>
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<h5>Average Completion Time</h5>
<p>2 Weeks</p>
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<h5>Accreditation</h5>
<p>3 CPD Hours</p>
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<h5>Level</h5>
<p>Advanced</p>
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<h5>Start Time</h5>
<p>Anytime</p>
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<h5>100% Online</h5>
<p>Study online with ease.</p>
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<h5>Unlimited Access</h5>
<p>24/7 unlimited access with pre-recorded lectures.</p>
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<h5>Low Fees</h5>
<p>Our fees are low and easy to pay online.</p>
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Metropolitan School of Business & Management UK

Business Analytics & Intelligence

Big Data Analytics

Data Science

Marketing Analytics

Diploma in Management Studies (DMS)

Data Analyst

Professional Certificate Course in Big Data Analytics in London 2024

General Admission

<p><strong>***24 Hour Limited Time Flash Sale***<br /><br /></strong><strong> Basics of Data Science - CPD Certified 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>In a world where<strong> 2.5 quintillion </strong>bytes of data are produced every day, how can professionals leverage this data for strategic decision-making and competitive advantage? In the UK, the data science industry is booming, with an estimated increase in demand for data scientists and analytics professionals by over <strong>28% by 2020. </strong>This Basics of Data Science bundle gives you diverse analytical skills to launch or advance your analytics career.</p><p>Designed to cater to the needs of both seasoned professionals and aspiring newcomers, our Basics of Data Science 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 Basics of Data Science Online Training, you'll receive 30 CPD-Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free.</strong></p><p><strong>Courses Are Included In this Bundle:</strong></p><ul><li><strong>Course 01: </strong>Diploma in Data Analysis Fundamentals</li><li><strong>Course 02: </strong>Business Intelligence and Data Mining</li><li><strong>Course 03: </strong>Google Data Studio: Data Analytics</li><li><strong>Course 04: </strong>Statistics</li><li><strong>Course 05: </strong>Statistical Analysis</li><li><strong>Course 06: </strong>Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 07: </strong>Microsoft Power BI - Master Power BI in 90 Minutes!</li><li><strong>Course 08: </strong>R Programming for Data Science</li><li><strong>Course 09: </strong>PowerBI Formulas</li><li><strong>Course 10:</strong>Time Management Training - Online Course</li><li><strong>Course 11: </strong>Excel Data Analysis</li><li><strong>Course 12: </strong>Stock Trading Analysis with Volume Trading</li><li><strong>Course 13: </strong>Fundamentals of Business Analysis</li><li><strong>Course 14: </strong>Minute-Taking Executive Training</li><li><strong>Course 15: </strong>Financial Modeling Using Excel</li><li><strong>Course 16: </strong>Complete Communication Skills Master Class for Life</li><li><strong>Course 17: </strong>Public Speaking</li><li><strong>Course 18: </strong>Data Science &amp; Machine Learning with Python</li><li><strong>Course 19: </strong>Quick Data Science Approach from Scratch</li><li><strong>Course 20: </strong>Strategic Planning and Analysis for Marketing</li><li><strong>Course 21: </strong>Understanding Financial Statements and Analysis</li><li><strong>Course 22: </strong> Master JavaScript with Data Visualization</li><li><strong>Course 23: </strong> Receptionist Skills</li><li><strong>Course 24: </strong>Python Data Science</li><li><strong>Course 25: </strong>Research Methods in Business</li><li><strong>Course 26: </strong>Technical Analysis Masterclass for Trading &amp; Investing</li><li><strong>Course 27: </strong>SAS Programming Basic to Advanced</li><li><strong>Course 28: </strong> Investment Analyst</li><li><strong>Course 29: </strong>Financial Analysis</li><li><strong>Course 30: </strong>Excel Data Tools and Data Management</li></ul><p>With Basics of Data Science, 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><strong>Learning Outcomes of this Bundle</strong></p><ul><li>Collect, clean, and transform complex datasets using Python and R</li><li>Create insightful dashboards and visualizations in Power BI</li><li>Apply statistical techniques to interpret trends and make predictions</li><li>Build financial models to determine business value and opportunity</li><li>Automate analytical tasks through SAS and JavaScript</li><li>Communicate data-driven insights to stakeholders effectively</li></ul><p>Don't let this opportunity pass you by. Enrol in the Basics of Data Science today and take the first step towards achieving your goals and dreams.</p><p><strong>Why buy this Bundle?</strong></p><ul><li><strong>Free CPD Accredited Certificate</strong> upon completion of Basics of Data Science</li><li>Get <strong>a free student ID card</strong> with Basics of Data Science</li><li><strong>Lifetime access</strong> to the Basics of Data Science course materials</li><li>Get instant access to this Basics of Data Science course</li><li>Learn the Basics of Data Science from anywhere in the world</li><li>24/7 tutor support with the Basics of Data Science course.</li></ul> <p><strong>Start your learning journey straightaway!</strong></p><p>Basics of Data Science premium bundle consists of 30 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of Basics of Data Science 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 Basics of Data Science course. After passing the Basics of Data Science 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>

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

                        <div id="who_is_this_course_for">
                            <p>This Basics of Data Science course is ideal for:</p><ul><li>Students seeking mastery in the Basics of Data Science</li><li>Professionals seeking to enhance the Basics of Data Science skills</li><li>Individuals looking for a Basics of Data Science-related career.</li><li>Anyone passionate about the Basics of Data Science</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Basics of Data Science 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 Basics of Data Science bundle will allow you to kickstart or take your career in the related sector to the next stage.</p><ul><li>Data Analyst</li><li>Business Analyst</li><li>Data Scientist</li><li>Marketing Analyst</li><li>Financial Analyst</li></ul>
                        </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>

Academy for Health and Fitness

Online Course Marketing

Data Skills

Basics of Data Science - CPD Certified

<p>Are you ready to dive deep into the world of data analytics? Look no further than our Data Analytics with Tableau bundle, featuring 11 comprehensive courses that will give you the skills and knowledge needed to succeed in the world of data analysis.</p><p>Begin with the QLS-endorsed Advanced Diploma in Data Analytics with Tableau at QLS Level 6, and receive a free hardcopy certificate upon completion. This prestigious certification will set you apart from your peers.</p><p>Continue your learning journey with ten additional courses, all CPD-QS accredited, covering everything from SQL Masterclass to GDPR compliance. Our courses are designed to provide a theoretical understanding of data analytics that will serve as a strong foundation for your future career in the field.</p><p>Whether you're just starting out in data analytics or are a seasoned professional looking to expand your knowledge, our Data Analytics with Tableau bundle is the perfect choice for you. Enrol now and take the first step towards a brighter future in the exciting world of data analytics.</p><p><strong>This Data Analytics with Tableau Bundle Package includes:</strong></p><ul><li><strong>Course 01:</strong> Advanced Diploma in Data Analytics with Tableau at QLS Level 6</li></ul><p><strong>10 Premium Additional CPD QS Accredited Courses -</strong></p><ul><li><strong>Course 01:</strong> Data Analysis</li><li><strong>Course 02:</strong> SQL Masterclass: SQL For Data Analytics</li><li><strong>Course 03:</strong> Data Science &amp; Machine Learning with R Training</li><li><strong>Course 04:</strong> Google Data Studio</li><li><strong>Course 05:</strong> Excel Data Analysis</li><li><strong>Course 06:</strong> Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query</li><li><strong>Course 07:</strong> Business Intelligence and Data Mining Diploma</li><li><strong>Course 08:</strong> Business Data Analysis</li><li><strong>Course 09:</strong> Statistical Analysis</li><li><strong>Course 10:</strong> GDPR</li></ul><p><strong>Why Prefer This Data Analytics with Tableau Bundle?</strong></p><ul><li>You will receive a completely free certificate from the Quality Licence Scheme</li><li>Option to purchase 10 additional certificates accredited by CPD</li><li>Get a free Student ID Card - (£10 postal charges will be applicable for international delivery)</li><li>Free assessments and immediate success results</li><li>24/7 Tutor Support</li></ul> <p><strong>After taking this Data Analytics with Tableau</strong><strong> bundle courses, you will be able to learn:</strong></p><ul><li>Gain a thorough understanding of data analytics concepts and tools.</li><li>Master Tableau and Excel for data analysis and visualisation.</li><li>Learn how to work with SQL and relational databases for data management.</li><li>Develop skills in data science and machine learning using R.</li><li>Understand the principles of business intelligence and data mining.</li><li>Gain knowledge of statistical analysis and interpretation of results.</li><li>Acquire a comprehensive understanding of GDPR compliance and regulations.</li></ul><p><strong>***Curriculum breakdown of Data Analytics with Tableau</strong><strong>***</strong></p><ul><li><strong>Module 01:</strong> Introduction to the Course</li><li><strong>Module 02:</strong> Project 1: Discount Mart (Sales and Profit Analytics)</li><li><strong>Module 03: </strong>Project 2: Green Destinations (HR Analytics)</li><li><strong>Module 04:</strong> Project 3: Superstore (Sales Agent Tracker)</li><li><strong>Module 05: </strong>Northwind Trade (Shipping Analytics)</li><li><strong>Module 06: </strong>Project 5: Tesla (Stock Price Analytics)</li><li><strong>Module 07:</strong> Bonus: Introduction to Database Concepts</li><li><strong>Module 08: </strong>Tableau Stories</li></ul><p><strong>How is the Data Analytics with Tableau</strong><strong>Bundle Assessment Process?</strong></p><p>You have to complete the assignment questions given at the end of the course and score a minimum of 60% to pass each exam. Our expert trainers will assess your assignment and give you feedback after you submit the assignment.</p><p>You will be entitled to claim a certificate endorsed by the Quality Licence Scheme after you successfully pass the exams.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Individuals interested in pursuing a career in data analytics.</li><li>Professionals seeking to enhance their data analysis skills.</li><li>Business analysts, data scientists, and statisticians.</li><li>Anyone looking to gain a theoretical foundation in data analytics.</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £20k-£48k per year</li><li>Business Intelligence Analyst: £24k-£60k per year</li><li>Data Scientist: £33k-£75k per year</li><li>Business Analyst: £24k-£50k per year</li><li>Data Engineer: £28k-£80k per year</li><li>Statistician: £25k-£45k per year</li></ul><p>Note: Salary ranges are subject to change depending on location, experience, and industry.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Advanced Diploma in Data Analytics with Tableau at QLS Level 6</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>After successfully completing the course, you can order an original hardcopy Certificate of Achievement endorsed by the <strong>Quality Licence Scheme</strong>. This certificate will be delivered to your home, free of charge.</p><p>To obtain the additional Quality License Scheme Endorsed Certificate, you will need to pay the corresponding fee based on the level of the courses:</p><ul><li>Level 1: £59</li><li>Level 2: £79</li><li>Level 3: £89</li><li>Level 4: £99</li><li>Level 5: £119</li><li>Level 6: £129</li><li>Level 7: £139</li></ul> </div>
        <h4>CPD QS Accredited Certificate</h4>
        <p>
            Digital certificate -
            £10
        </p>
            <div> <p>Upon successfully completing the Bundle, you will need to place an order to receive a PDF Certificate for each course within the bundle. These certificates serve as proof of your newly acquired skills, accredited by CPD QS and will cost you £10 for each course. Also, the certificates are recognised throughout the UK and internationally.</p> </div>
        <h4>CPD QS Accredited Certificate</h4>
        <p>
            Hard copy certificate -
            £29
        </p>
            <div> <p>International students are subject to a £10 delivery fee for their orders, based on their location.</p> </div>

                        </div>

Data Analytics with Tableau Diploma

<p><strong>***24 Hour Limited Time Flash Sale***<br /><br /></strong><strong> Google Analytics Course - Taking Your Business to the Next Level Course 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 Google Analytics for Beginners - Master Google Analytics 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 Google Analytics for Beginners - Master Google Analytics Course 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 Google Analytics Course - Taking Your Business to the Next Level Course Online Training, you'll receive 25 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 Google Analytics Course - Taking Your Business to the Next Level Course Career Bundle:</strong></p><ul><li><strong>Course 01: </strong>Google Analytics for Everyone</li><li><strong>Course 02: </strong>Google Data Studio: Data Analytics</li><li><strong>Course 03: </strong>Business Data Analysis</li><li><strong>Course 04:</strong>Big Data Analytics with PySpark Tableau Desktop and MongoDB</li><li><strong>Course 05: </strong>Big Data Analytics with PySpark Power BI and MongoDB</li><li><strong>Course 06: </strong>Introduction to Data Analytics with Tableau</li><li><strong>Course 07: </strong>Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau</li><li><strong>Course 08: </strong>Develop Big Data Pipelines with R, Sparklyr &amp; Power BI</li><li><strong>Course 09: </strong>Quick Data Science Approach from Scratch</li><li><strong>Course 10: </strong>Machine Learning with Python Course</li><li><strong>Course 11: </strong>Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li><strong>Course 12: </strong>Data Science &amp; Machine Learning with Python</li><li><strong>Course 13: </strong>SQL for Data Science, Data Analytics, and Data Visualization</li><li><strong>Course 14: </strong>Customer Analytics Training</li><li><strong>Course 15: </strong>Statistical Analysis</li><li><strong>Course 16: </strong>Excel Data Analysis</li><li><strong>Course 17: </strong>Excel Pivot Tables, Pivot Charts, Slicers, and Timelines</li><li><strong>Course 18: </strong>Business Intelligence and Data Mining Diploma</li><li><strong>Course 19: </strong>How To Do Keyword Research For SEO &amp; Ranking On Google</li><li><strong>Course 20: </strong>Digital Marketing for Business</li><li><strong>Course 21: </strong>Digital Marketing Starting an Online E-Commerce Store</li><li><strong>Course 22: </strong>Marketing Strategies for Business</li><li><strong>Course 23: </strong>Strategic Planning and Analysis for Marketing</li><li><strong>Course 24: </strong>Ultimate Social Media Marketing course</li><li><strong>Course 25: </strong>Bootstrap - Responsive Web Design</li></ul><p>With Google Analytics Course - Taking Your Business to the Next Level Course, 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 Google Analytics Course - Taking Your Business to the Next Level Course today and take the first step towards achieving your goals and dreams.</p><p><strong>Why buy this Google Analytics Course - Taking Your Business to the Next Level Course?</strong></p><ul><li><strong>Free CPD Accredited Certificate</strong> upon completion of Google Analytics Course - Taking Your Business to the Next Level Course</li><li>Get <strong>a free student ID card</strong> with Google Analytics Course - Taking Your Business to the Next Level Course</li><li><strong>Lifetime access</strong> to the Google Analytics Course - Taking Your Business to the Next Level Course materials</li><li>Learn Google Analytics Course - Taking Your Business to the Next Level Course from anywhere in the world</li><li>24/7 tutor support with the Google Analytics Course - Taking Your Business to the Next Level Course.</li></ul> <p><strong>Start your learning journey straightaway with our Google Analytics for Beginners - Master Google Analytics Course Training!</strong></p><p>Google Analytics Course - Taking Your Business to the Next Level Course premium bundle consists of 25 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of Google Analytics Course - Taking Your Business to the Next Level Course 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 course. After passing the Google Analytics Course - Taking Your Business to the Next Level Course 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>
                                        250 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This bundle 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 Google Analytics Course - Taking Your Business to the Next Level Course 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 Google Analytics Course - Taking Your Business to the Next Level Course 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>

Google Data Analytics

Google Analytics Individual Qualification

Google Analytics

Google Digital Marketing

Google Analytics for Beginners - Master Google Analytics

<section style="color: rgb(33, 37, 41); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(33, 37, 41);"><h2 style="color: rgb(62, 57, 52); box-sizing: border-box;" fr-original-style=" color: rgb(62, 57, 52);">About this training course</h2><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Gas-lift is one of the predominant forms of artificial lift used for lifting liquids from conventional, unconventional, onshore and offshore assets. Gas-lift and its various forms (intermittent lift, gas-assisted plunger lift) allows life of well lift-possibilities when selected and applied properly. This 5-day training course is designed to give participants a thorough understanding of gas-lift technology and related application concepts.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This training course covers main components such as application envelope, relative strengths and weaknesses of gas-lift and its different forms like intermittent lift, gas-assisted plunger lift. Participants solve examples and class problems throughout the course. Animations and videos reinforce the concepts under discussion.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Unique Features:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Hands-on usage of&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">SNAP Software</strong>&nbsp;to solve gas-lift exercises</li><li fr-original-style="" style="box-sizing: border-box;">Discussion on&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">digital oil field</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Machine learning</strong>&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">applications</strong>&nbsp;in gas-lift optimization</li></ul></section><p id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><a href="https://petroedgeasia.net/training/gas-lift-design-optimization-using-nodal-analysis-classroom-training/" fr-original-style="" style="user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;">Download your brochure</a></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><a href="mailto:info@asiaedge.net?subject=Please Send me the Brochure for Gas Lift Design & Optimization using NODAL Analysis" fr-original-style="" style="user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;">Email me the brochure</a></p><section style="color: rgb(33, 37, 41); box-sizing: border-box;" fr-original-style=" color: rgb(33, 37, 41);"><a data-toggle="collapse" href="https://petroedgeasia.net/training/gas-lift-design-optimization-using-nodal-analysis-classroom-training/#collapseOne" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);">TRAINING OBJECTIVES</a><div data-parent="#accordion" fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">After the completion of this training course, participants will be able to:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Understand the fundamental theories and procedures related to Gas-Lift operations</li><li fr-original-style="" style="box-sizing: border-box;">Easily recognize the different components of the gas-lift system and their basic structural and operational features</li><li fr-original-style="" style="box-sizing: border-box;">Be able to design a gas-lift installation</li><li fr-original-style="" style="box-sizing: border-box;">Comprehend how digital oilfield tools help address ESP challenges</li><li fr-original-style="" style="box-sizing: border-box;">Examine recent advances in real-time approaches to the production monitoring and lift management</li></ul></div><a data-toggle="collapse" href="https://petroedgeasia.net/training/gas-lift-design-optimization-using-nodal-analysis-classroom-training/#collapseTwo" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);">TARGET AUDIENCE</a><div data-parent="#accordion" fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This training course is suitable and will greatly benefit the following specific groups:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Production, reservoir, completion, drilling and facilities engineers, analysts, and operators</li><li fr-original-style="" style="box-sizing: border-box;">Anyone interested in learning about implications of gas-lift systems for their fields and reservoirs</li></ul></div><a data-toggle="collapse" href="https://petroedgeasia.net/training/gas-lift-design-optimization-using-nodal-analysis-classroom-training/#collapsecl" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);">COURSE LEVEL</a><div data-parent="#accordion" fr-original-style="" style="box-sizing: border-box;"><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Intermediate</li><li fr-original-style="" style="box-sizing: border-box;">Advanced</li></ul></div><a data-toggle="collapse" href="https://petroedgeasia.net/training/gas-lift-design-optimization-using-nodal-analysis-classroom-training/#collapseThree" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);">TRAINING METHODS</a><div data-parent="#accordion" fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The training instructor relies on a highly interactive training method to enhance the learning process. This method ensures that all participants gain a complete understanding of all the topics covered. The training environment is highly stimulating, challenging, and effective because the participants will learn by case studies which will allow them to apply the material taught in their own organization.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><em fr-original-style="" style="box-sizing: border-box;">Course Duration: 5 days in total (35 hours).</em></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Training Schedule</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">0830 &ndash; Registration&nbsp;0900 &ndash; Start of training&nbsp;1030 &ndash; Morning Break&nbsp;1045 &ndash; Training recommences&nbsp;1230 &ndash; Lunch Break&nbsp;1330 &ndash; Training recommences&nbsp;1515 &ndash; Evening break&nbsp;1530 &ndash; Training recommences&nbsp;1700 &ndash; End of Training</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The maximum number of participants allowed for this training course is 20.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This course is also available through our Virtual Instructor Led Training (VILT) format.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Prerequisites:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Understanding of petroleum production concepts.</li><li fr-original-style="" style="box-sizing: border-box;">Each participant needs a laptop/PC for solving class examples using software to be provided during class. Laptop/PC needs to have a current Windows operating system and at least 500 MB free disk space. Participants should have administrator rights to install software.</li></ul></div><a data-toggle="collapse" href="https://petroedgeasia.net/training/gas-lift-design-optimization-using-nodal-analysis-classroom-training/#collapseFive" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);">TRAINER</a><div data-parent="#accordion" fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Your expert course leader has over 35 years&rsquo; work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor&rsquo;s and Master&rsquo;s in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Highlighted Work Experience:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS &ndash; a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe.</li><li fr-original-style="" style="box-sizing: border-box;">Worked with and consulted on equipment&rsquo;s like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements.</li><li fr-original-style="" style="box-sizing: border-box;">Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics.</li><li fr-original-style="" style="box-sizing: border-box;">Developed a workshop on digital oilfield approaches for production engineers.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Patents:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Principal inventor: &ldquo;Smarter Slug Flow Conditioning and Control&rdquo;</li><li fr-original-style="" style="box-sizing: border-box;">Co-inventor: &ldquo;Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells&rdquo;</li><li fr-original-style="" style="box-sizing: border-box;">Co-inventor: &ldquo;Wellbore real-time monitoring and analysis of fracture contribution&rdquo;</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Worldwide Experience in Training / Seminar / Workshop Deliveries:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&amp;T, Louisiana State, U of Southern California, and U of Houston.</li><li fr-original-style="" style="box-sizing: border-box;">He has conducted seminars, bespoke trainings / workshops globally for practicing professionals:<ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><u fr-original-style="" style="box-sizing: border-box;">Companies:</u>&nbsp;Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others.</li><li fr-original-style="" style="box-sizing: border-box;"><u fr-original-style="" style="box-sizing: border-box;">Countries:</u>&nbsp;USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela.</li><li fr-original-style="" style="box-sizing: border-box;">Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic.</li></ul></li></ul></div><a data-toggle="collapse" href="https://petroedgeasia.net/training/gas-lift-design-optimization-using-nodal-analysis-classroom-training/#collapseCS" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);"></a></section><p id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><a href="https://petroedgeasia.net/training/gas-lift-design-optimization-using-nodal-analysis-classroom-training/" fr-original-style="" style="user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;">Download your brochure</a></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><a href="mailto:info@asiaedge.net?subject=Please Send me the Brochure for Gas Lift Design & Optimization using NODAL Analysis" fr-original-style="" style="user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;">Email me the brochure</a></p><section style="color: rgb(33, 37, 41); box-sizing: border-box;" fr-original-style=" color: rgb(33, 37, 41);"><a data-toggle="collapse" href="https://petroedgeasia.net/training/gas-lift-design-optimization-using-nodal-analysis-classroom-training/#collapseCS" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);">POST TRAINING COACHING SUPPORT (OPTIONAL)</a><div data-parent="#accordion" fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">To further optimise your learning experience from our courses, we also offer individualized &ldquo;One to One&rdquo; coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.<br fr-original-style="" style="box-sizing: border-box;"><a href="mailto:info@asiaedge.net?subject=Request%20for%20further%20information%20regarding%20post%20training%20coaching%20support" target="_blank" style="color: rgb(0, 123, 255); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(0, 123, 255);">Request for further information post training support</a>&nbsp;and fees applicable</p></div></section>

EnergyEdge - Training for a Sustainable Energy Future

Elevate your knowledge in gas lift design optimization with EnergyEdge's classroom training. Join us to stay ahead in the industry!

Gas Lift Design & Optimization using NODAL Analysis

Team of 10, pp

Normal Fee, pp

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Export Data</li><li>Backup And Restore Database</li></ul><p><strong>**Data Analysis In Excel**</strong></p><ul><li>Modifying A Worksheet</li><li>Working With Lists</li><li>Analyzing Data</li><li>Visualizing Data With Charts</li><li>Using PivotTables And PivotCharts</li><li>Working With Multiple Worksheets And Workbooks</li><li>Using Lookup Functions And Formula Auditing</li><li>Automating Workbook Functionality</li><li>Creating Sparklines And Mapping Data</li><li>Forecasting Data</li></ul><p><strong>**Diploma in Data Analysis Fundamentals**</strong></p><ul><li>Introduction</li><li>Agenda And Principles Of Process Management</li><li>The Voice Of The Process</li><li>Working As One Team For Improvement</li><li>Exercise: The Voice Of The Customer</li><li>Tools For Data Analysis</li><li>The Pareto Chart</li><li>The Histogram</li><li>The Run Chart</li><li>Exercise: Presenting Performance Data</li><li>Understanding Variation</li><li>The Control Chart</li><li>Control Chart Example</li><li>Control Chart Special Cases</li><li>Interpreting The Control Chart</li><li>Control Chart Exercise</li><li>Strategies To Deal With Variation</li><li>Using Data To Drive Improvement</li><li>A Structure For Performance Measurement</li><li>Data Analysis Exercise</li><li>Course Project</li><li>Test Your Understanding</li></ul><p><strong>**Business Data Analysis**</strong></p><ul><li>Introduction To Business Analysis</li><li>Business Environment</li><li>Business Processes</li><li>Business Analysis Planning And Monitoring</li><li>Strategic Analysis And Product Scope</li><li>Solution Evaluation</li><li>Investigation Techniques</li><li>Ratio Analysis</li><li>Stakeholder Analysis And Management</li><li>Process Improvement With Gap Analysis</li><li>Documenting And Managing Requirements</li><li>Business Development And Succession Planning</li><li>Planning &amp; Forecasting Operations</li><li>Business Writing Skills</li></ul><p><strong>**Data Science &amp; Machine Learning with Python**</strong></p><ul><li>Course Overview &amp; Table Of Contents</li><li>Introduction To Machine Learning</li><li>System And Environment Preparation</li><li>Learn Basics Of Python - Assignment<br /><ul><li>Functions</li><li>Data Structures</li><li>NumPy Array</li><li>NumPy Data</li><li>NumPy Arithmetic</li><li>Learn Basics Of Matplotlib</li></ul></li><li>Learn Basics Of Pandas</li><li>Understanding The CSV Data File</li><li>Load And Read CSV Data File Using Python Standard Library<br /><ul><li>Using NumPy</li><li>Using Pandas</li></ul></li><li>Dataset Summary - Peek, Dimensions And Data Types<br /><ul><li>Class Distribution And Data Summary</li><li>Explaining Correlation</li><li>Explaining Skewness - Gaussian And Normal Curve</li></ul></li><li>Dataset Visualization - Using Histograms</li><li>Dataset Visualization - Using Density Plots</li><li>Dataset Visualization - Box And Whisker Plots</li><li>Multivariate Dataset Visualization - Correlation Plots</li><li>Multivariate Dataset Visualization - Scatter Plots</li><li>Data Preparation (Pre-Processing) - Introduction<br /><ul><li>Re-Scaling Data</li><li>Standardizing Data</li><li>Normalizing Data</li><li>Binarizing Data</li></ul></li><li>Feature Selection - Introduction<br /><ul><li>Uni-Variate Part</li><li>Recursive Feature Elimination</li><li>Principal Component Analysis (PCA)</li><li>Feature Importance</li></ul></li><li>Refresher Session - The Mechanism Of Re-Sampling, Training And Testing</li><li>Algorithm Evaluation Techniques - Introduction<br /><ul><li>Train And Test Set</li><li>K-Fold Cross Validation</li><li>Leave One Out Cross Validation</li><li>Repeated Random Test-Train Splits</li></ul></li><li>Algorithm Evaluation Metrics - Introduction<br /><ul><li>Classification Accuracy</li><li>Log Loss</li><li>Area Under ROC Curve</li><li>Confusion Matrix</li><li>Classification Report</li><li>Mean Absolute Error - Dataset Introduction</li><li>Mean Absolute Error</li><li>Mean Square Error</li><li>R Squared</li></ul></li><li>Classification Algorithm Spot Check - Logistic Regression<br /><ul><li>Linear Discriminant Analysis</li><li>K-Nearest Neighbors</li><li>Naive Bayes</li><li>CART</li><li>Support Vector Machines</li></ul></li><li>Regression Algorithm Spot Check - Linear Regression<br /><ul><li>Ridge Regression</li><li>Lasso Linear Regression</li><li>Elastic Net Regression</li><li>K-Nearest Neighbors</li><li>CART</li><li>Support Vector Machines (SVM)</li></ul></li><li>Compare Algorithms</li><li>Pipelines</li><li>Performance Improvement: Ensembles - Voting<br /><ul><li>Bagging</li><li>Boosting</li></ul></li><li>Performance Improvement: Parameter Tuning Using Grid Search</li><li>Performance Improvement: Parameter Tuning Using Random Search</li><li>Export, Save And Load Machine Learning Models : Pickle</li><li>Export, Save And Load Machine Learning Models : Joblib</li><li>Finalizing A Model - Introduction And Steps</li><li>Finalizing A Classification Model - The Pima Indian Diabetes Dataset</li><li>Quick Session: Imbalanced Data Set - Issue Overview And Steps</li><li>Iris Dataset : Finalizing Multi-Class Dataset</li><li>Finalizing A Regression Model - The Boston Housing Price Dataset</li><li>Real-Time Predictions: Using The Pima Indian Diabetes Classification Model</li><li>Real-Time Predictions: Using Iris Flowers Multi-Class Classification Dataset</li><li>Real-Time Predictions: Using The Boston Housing Regression Model</li></ul><p><strong>**R Programming for Data Science**</strong></p><ul><li>Data Science Overview</li><li>R And RStudio</li><li>Introduction To Basics</li><li>Vectors</li><li>Matrices</li><li>Factors</li><li>Data Frames</li><li>Lists</li><li>Relational Operators</li><li>Logical Operators</li><li>Conditional Statements</li><li>Loops</li><li>Functions</li><li>R Packages</li><li>The Apply Family - Lapply</li><li>The Apply Family - Sapply &amp; Vapply</li><li>Useful Functions</li><li>Regular Expressions</li><li>Dates And Times</li><li>Getting And Cleaning Data</li><li>Plotting Data In R</li><li>Data Manipulation With Dplyr</li></ul><p><strong>**Learn Python, JavaScript, and Microsoft SQL for Data science**</strong></p><ul><li>JavaScript Introduction</li><li>JavaScript Basics</li><li>JavaScript Operators</li><li>JavaScript Conditional Statements</li><li>JavaScript Control Flow Statements</li><li>JavaScript Functions</li><li>JavaScript Error Handling</li><li>JavaScript Client-Side Validations</li><li>Python Introduction</li><li>Python Basic</li><li>Python Strings</li><li>Python Operators</li><li>Python Data Structures</li><li>Python Conditional Statements</li><li>Python Control Flow Statements</li><li>Python Core Games</li><li>Python Functions</li><li>Python Args, KW Args For Data Science</li><li>Python Project</li><li>Python Object Oriented Programming [OOPs]</li><li>Python Methods</li><li>Python Class And Objects</li><li>Python Inheritance And Polymorphism</li><li>Python Encapsulation And Abstraction</li><li>Python OOPs Games</li><li>Python Modules And Packages</li><li>Python Error Handling</li><li>Microsoft SQL[MS] Introduction</li><li>MS SQL Statements</li><li>MS SQL Filtering Data</li><li>MS SQL Functions</li><li>MS SQL Joins</li><li>MS SQL Advanced Commands</li><li>MS SQL Structure And Keys</li><li>MS SQL Queries</li><li>MS SQL Structure Queries</li><li>MS SQL Constraints</li><li>MS SQL Backup And Restore</li></ul><h2>
        
        <div>CPD</div>
    </h2>

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

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

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

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

One Education

Data Analyst : Data Analyst Course

<p><strong>Do you want to learn software development but don't know where to start? Are you looking for a career change, but don't know how to get your foot in the door? If so, then our Software Development Training program is perfect for you!</strong></p><p>Our comprehensive Software Development Training offers more than just coding skills; we provide you with personalised mentoring, job opportunities, and unwavering support throughout your professional journey.</p><p>At studyhub, we understand that becoming a successful software developer involves more than just learning programming languages. That's why our program goes beyond the basics and focuses on empowering you with the tools, knowledge, and confidence to excel in this ever-evolving field. We will help you until you find a job, so you won't have to worry about a thing. Just gain knowledge and learn skills, getting you a job is our duty.</p><p>We are committed to helping you succeed in your career. That's why we offer our program with a 100% satisfaction guarantee. Don't wait any longer! Enrol in our Software Development Training program today and start your journey to a successful career in software development!</p><p><strong>Our mission is simple:</strong> to be your trusted partner and assist you every step of the way until you land the job of your dreams.<br /><br /></p><p><strong>Why Choose Us</strong></p><p>Here's what makes our Software Development Training Program stand out from the crowd:</p><ul><li><strong>Personalised Job Search Assistance:</strong> We're in this together! Our dedicated team will work tirelessly to support your entire job searching process. From crafting a standout resume to submitting it to top companies, we'll ensure you have a compelling application that gets noticed.</li></ul><ul><li><strong>Expertly Crafted CV:</strong> Your resume is your ticket to the interview room. Our professional resume writers will create a customised CV highlighting your unique skills and experiences. This will maximise your chances of standing out among the competition.</li></ul><ul><li><strong>Strategic Placement:</strong> We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals.</li></ul><ul><li><strong>One-On-One Consultation Sessions with Industry Experts:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the Software Development field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.</li></ul><ul><li><strong>Comprehensive Skill Development:</strong> Our program is designed to equip you with the most sought-after skills in the Software Development sector. From mastering cutting-edge tools to honing your Software Development prowess, we'll ensure you have the knowledge and expertise to excel in any Software-driven environment.</li></ul><ul><li><strong>Ongoing Support:</strong> We understand that the journey to landing your dream job doesn't end with placement. That's why our commitment to your success extends well beyond your initial training. Our support team will be available to answer your questions, provide guidance, and assist you as you progress in your career.</li></ul><p><strong>Here are the 6 courses we will provide once you enrol in the program:</strong></p><ul><li>C# (C-Sharp)</li><li>Refactor Javascript Course</li><li>Python Course</li><li>PHP Web Development with MySQL; GitHub &amp; Heroku</li><li>ASP.Net MVC and Entity Framework Course</li><li>Machine Learning</li></ul><p>These courses will help you to develop your knowledge and skills to become a successful Software Developer.</p> <p>The Program is completed in 5 easy steps:</p><p><strong>Step 1 - Enrol in the program and start learning from the courses we provide</strong></p><p>After you enrol in this Software Development Training Program, you will get lifetime access to 6 premium courses related to Software Development. These courses will teach you the knowledge and skills required to become a successful Software Developer. Our customer service team will help you and keep in contact with you every step of the way.</p><p><strong>Step 2 - Complete your courses and get certifications</strong></p><p>After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks for the first course and 80% for the rest relevant courses. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.</p><p><strong>Step 3 - Get a consultation session from a professional Software Developer</strong></p><p>Take your Software Development skills to new heights with a one-on-one consultation session led by a seasoned professional. Gain invaluable insights, expert tips, and tailored advice that will propel your career forward.</p><p><strong>Step 4 - Complete the CV and attend the interview</strong></p><p>Once you've successfully obtained the certifications, our team of professionals will build you a compelling CV and LinkedIn profile. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently. Kickstart your Software Development career with a starting salary ranging from <strong>•30,000 to •50,000</strong> annually. Step into the Software Development industry with the assurance of a promising future.</p><p><strong>Step 5 - We will not leave you until you get a job</strong></p><p>Our commitment to your success goes above and beyond. We won't stop until you land that dream job. With personalised support, expert guidance, and unwavering dedication, we'll be by your side until you secure the perfect opportunity. Your job search becomes our mission, ensuring you have the best chance at a successful career in Software Development.</p><p>Don't let your dream career as a Software Developer remain out of reach. You just have to learn and gain knowledge and skills. Finding a job is our task! We will remove most of your worries and help you focus on only developing your skills. So join our Software Development Training Program today, and let us be your trusted partner on your journey to success.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>This Software Development Training program is ideal for:</p><ul><li>Aspiring software developers seeking comprehensive training and mentorship.</li><li>Career changers seeking to transition into the software development field.</li><li>Recent graduates aiming to enhance their technical skills and improve their employability.</li><li>Professionals in related fields who want to expand their knowledge and delve into software development.</li><li>Individuals passionate about coding and eager to turn their hobby into a fulfilling profession.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

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

                        <div id="career_path">
                            <p>Upon completing our program, you'll be equipped to pursue various software development roles in the UK, such as software engineer, web developer, mobile app developer, database administrator, software tester, or software project manager. With the demand for skilled developers constantly on the rise, you'll have ample opportunities to thrive and grow in your chosen career path.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Studyhub UK

Software Engineer

Learning & Development

Online Course Creation

Software Developer

Computing & Software Development Tools

Software Development

Software Development - IT Job Ready Program + Career Support & Money Back Guarantee

<p>AI can create art! There are AI algorithms and models that have been trained to generate artwork, music, and even poetry. Dive deep into the world of AI, starting from the basics and progressing to cutting-edge technologies. From understanding the mathematics that underpin AI to exploring machine learning, deep learning, and real-world applications, this course equips you with the knowledge you need to thrive in this dynamic field. Whether you're a tech enthusiast or a professional looking to enhance your skills, this course is your gateway to a future where AI shapes the world.</p><p><strong>Key Features</strong></p><ul><li>CPD Accredited</li><li>FREE PDF + Hardcopy certificate</li><li>Fully online, interactive course</li><li>Self-paced learning and laptop, tablet and smartphone-friendly</li><li>24/7 Learning Assistance</li><li>Discounts on bulk purchases</li></ul><p><strong>Course Curriculum </strong></p><ul><li>Module 01 : Introduction to Artificial Intelligence</li><li>Module 02 : Mathematics for AI</li><li>Module 03 : Knowledge Representation in AI - Part 1</li><li>Module 04 : Knowledge Representation in AI - Part 2</li><li>Module 05 : Machine Learning - Part 1</li><li>Module 06 : Machine Learning - Part 2</li><li>Module 07 : Deep Learning</li><li>Module 08 : Natural Language Processing</li><li>Module 09 : Computer Vision</li><li>Module 10 : Robotics</li><li>Module 11 : Building AI Applications</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Grasp the fundamentals of artificial intelligence and its applications.</li><li>Develop a strong mathematical foundation for AI algorithms.</li><li>Master knowledge representation techniques in AI.</li><li>Explore the principles and applications of machine learning.</li><li>Dive into the world of deep learning and its use in AI.</li><li>Understand the core concepts of natural language processing and computer vision.</li></ul><p><strong>Accreditation</strong></p><p>This course is <strong>CPD Quality Standards</strong> (<strong>CPD QS)</strong> accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 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>Technology enthusiasts eager to delve into AI.</li><li>Students pursuing a career in AI and machine learning.</li><li>Professionals seeking to upskill in the AI domain.</li><li>Engineers and programmers interested in AI development.</li><li>Entrepreneurs exploring AI for business solutions.</li><li>Anyone with a curiosity about the future of artificial intelligence.</li><li>Graduates looking to enhance their tech-related knowledge.</li><li>Innovators with an interest in robotics and AI applications.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>AI Research Scientist</li><li>Machine Learning Engineer</li><li>Data Scientist</li><li>Natural Language Processing Engineer</li><li>Computer Vision Specialist</li><li>Robotics Software Engineer</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p>Once you've successfully completed your course, you will immediately be sent a FREE digital certificate.</p></div>
        <h4>Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>Also, you can have your FREE printed certificate delivered by post (shipping cost £3.99 in the UK).<br />For all international addresses outside of the United Kingdom, the delivery fee for a hardcopy certificate will be only <strong>£10</strong>.<br />Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.</p></div>

                        </div>

Training Express

Foundations of Artificial Intelligence Course

<h2>Building Data Science Products? Think Business First</h2>
<p>Modern machine learning libraries are both a blessing and a curse. Due to the ease with which the libraries can be used, most users (newbies and practitioners alike) focus too much on tools and techniques. We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies.Learning Objectives We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies.</p>
<p>This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs.</p>
<p>Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies.</p>
<p>Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.</p>

IIL Europe Ltd

Building Data Science Products? Think Business First

Packt

Python

Deep Learning

Use deep learning, Computer Vision, and machine learning techniques to build an autonomous car with Python

The Complete Self-Driving Car Course - Applied Deep Learning

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

Nexus Human

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

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

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

Machine Learning (ML)

Machine Learning for Apps Level 4

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

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

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

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

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

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

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

Python Machine Learning Crash Course for Beginners

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

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

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

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels</p>
        <h2>What You Will Learn</h2><p>Confidently work with two of the leading ML packages: statsmodels and sklearn <br>Understand how to perform a linear regression <br>Become familiar with the ins and outs of logistic regression <br>Get to grips with carrying out cluster analysis (both flat and hierarchical) <br>Apply your skills to real-life business cases <br>Get insights into the underlying ideas behind ML models</p>
        <h2>Audience</h2><p>If you want to get acquainted with fundamental machine learning methods, become a successful data scientist, or just get started with building valuable skills in machine learning and data science, this course is for you.</p>
        <h2>Approach</h2><p>The course has a perfect blend of theory and practical knowledge to help you gain a solid understand of the concepts covered.</p>
        <h2>Key Features</h2><p>Learn machine learning with StatsModels and sklearn  * Apply machine learning skills to solve real-world business cases  * Get started with linear regression, logistic regression, and cluster analysis</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-101-with-scikit-learn-and-statsmodels</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Setting Up the Working Environment</h4><p></p><br><h4>3. Linear Regression with StatsModels</h4><p></p><br><h4>4. Linear Regression with Sklearn</h4><p></p><br><h4>5. Linear Regression - Practical Example</h4><p></p><br><h4>6. Logistic Regression</h4><p></p><br><h4>7. Cluster Analysis</h4><p></p><br><h4>8. Cluster Analysis: Additional Topics</h4><p></p>

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

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

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

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

Machine Learning for Absolute Beginners - Level 2

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

TensorFlow

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

The Complete Machine Learning Course with Python

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

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        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

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

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