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			<h2>Overview
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					<p><span>This comprehensive course on </span><b><span>Machine Learning for Predictive Maps in Python and Leaflet </span></b>will deepen your understanding on this topic.</p><p><span><span>After successful completion of this course you can acquire the required skills in this sector. This </span><b>Machine Learning for Predictive Maps in Python and Leaflet </b>comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.</span></p><p><span><span>So enrol in this course today to fast track your career ladder.</span></span></p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.</p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p><span>There is no experience or previous qualifications required for enrolment on this </span><b><span>Machine Learning for Predictive Maps in Python and Leaflet</span></b>. It is available to all students, of all academic backgrounds.</p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p><span>Our </span><b><span>Machine Learning for Predictive Maps in Python and Leaflet </span></b>is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</p><p><span><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></span></p>					</div>
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			<h2>Career Path</h2>		</div>
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					<p>Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to-</p><ul><li>Open doors of opportunities</li><li>Increase your adaptability</li><li>Keep you relevant</li><li>Boost confidence</li></ul><p>And much more!</p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>9 sections • 33 lectures • 05:59:00 total length</p></div><div><div><div><div><div><div><div><p>•Introduction: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Python Installation: 00:04:00</p></div><div><div><p>•Creating a Python Virtual Environment: 00:07:00</p></div><div><div><p>•Installing Django: 00:09:00</p></div><div><div><p>•Installing Visual Studio Code IDE: 00:06:00</p></div><div><div><p>•Installing PostgreSQL Database Server Part 1: 00:03:00</p></div><div><div><p>•Installing PostgreSQL Database Server Part 2: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Adding the settings.py Code: 00:07:00</p></div><div><div><p>•Creating a Django Model: 00:10:00</p></div><div><div><p>•Adding the admin.py Code: 00:21:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Creating Template Files: 00:10:00</p></div><div><div><p>•Creating Django Views: 00:10:00</p></div><div><div><p>•Creating URL Patterns for the REST API: 00:09:00</p></div><div><div><p>•Adding the index.html code: 00:04:00</p></div><div><div><p>•Adding the layout.html code: 00:19:00</p></div><div><div><p>•Creating our First Map: 00:10:00</p></div><div><div><p>•Adding Markers: 00:16:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Installing Jupyter Notebook: 00:07:00</p></div><div><div><p>•Data Pre-processing: 00:31:00</p></div><div><div><p>•Model Selection: 00:20:00</p></div><div><div><p>•Model Evaluation and Building a Prediction Dataset: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Creating a Django Model: 00:04:00</p></div><div><div><p>•Embedding the Machine Learning Pipeline in the Application: 00:42:00</p></div><div><div><p>•Creating a URL Endpoint for our Prediction Dataset: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Creating Multiple Basemaps: 00:09:00</p></div><div><div><p>•Creating the Marker Layer Group: 00:10:00</p></div><div><div><p>•Creating the Point Layer Group: 00:12:00</p></div><div><div><p>•Creating the Predicted Point Layer Group: 00:07:00</p></div><div><div><p>•Creating the Predicted High Risk Point Layer Group: 00:12:00</p></div><div><div><p>•Creating the Legend: 00:09:00</p></div><div><div><p>•Creating the Prediction Score Legend: 00:15:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Resource: 00:00:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Assignment - Machine Learning for Predictive Maps in Python and Leaflet: 00:00:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Apex Learning

Learning To Learn

Machine Learning for Predictive Maps in Python and Leaflet

<div><div id="commonContent"><p>ð Unlock the Power of Data with Our Machine Learning Course! ð¤</p><p>Are you ready to dive into the revolutionary world of Machine Learning? Welcome to our comprehensive course designed to equip you with the skills and knowledge needed to harness the potential of data-driven decision-making. ð</p><p>Machine Learning has rapidly emerged as one of the most transformative technologies of the 21st century. From powering intelligent virtual assistants to revolutionizing healthcare diagnostics, its applications are boundless. With our expertly crafted course, you'll embark on a journey that will demystify the complexities of Machine Learning and empower you to leverage its capabilities for diverse purposes. ð¡</p><h3></h3><h3>Why Machine Learning?</h3><p>In today's data-driven world, organizations across industries are seeking professionals who can extract actionable insights from vast amounts of data. Machine Learning offers the tools and techniques necessary to analyze complex datasets, identify patterns, and make predictions with unprecedented accuracy. By mastering Machine Learning, you'll gain a competitive edge in the job market and position yourself as a valuable asset to any organization. ð</p><h3></h3><h3>What You'll Learn:</h3><p>Our Machine Learning course covers a wide array of topics, including:</p><ul><li>Fundamentals of Machine Learning algorithms</li><li>Supervised, unsupervised, and reinforcement learning techniques</li><li>Data preprocessing and feature engineering</li><li>Model evaluation and validation</li><li>Deep learning and neural networks</li><li>Practical applications and case studies</li></ul><p>With hands-on projects and real-world examples, you'll not only understand the theory behind Machine Learning but also gain practical experience in implementing algorithms and solving complex problems. Whether you're a beginner or an experienced data professional, our course is tailored to accommodate learners of all levels. ð</p><h3></h3><h3>Who is this for?</h3><p>Our Machine Learning course is ideal for:</p><ul><li>Aspiring data scientists and analysts</li><li>Software engineers looking to transition into Machine Learning roles</li><li>Business professionals seeking to leverage data for strategic decision-making</li><li>Students and academics interested in exploring the forefront of technology</li></ul><p>No matter your background or experience level, our course provides a solid foundation in Machine Learning principles and techniques, setting you on the path to success in this rapidly evolving field. ð</p><h3>Career Path:</h3><p>By mastering Machine Learning, you'll open doors to a myriad of exciting career opportunities, including:</p><ul><li>Data Scientist</li><li>Machine Learning Engineer</li><li>AI Researcher</li><li>Business Intelligence Analyst</li><li>Data Engineer</li></ul><p>With the demand for Machine Learning professionals on the rise, employers are actively seeking individuals with the skills and expertise to drive innovation and deliver impactful solutions. Whether you're looking to advance your current career or embark on a new professional journey, our course will equip you with the tools and knowledge needed to thrive in today's competitive job market. ð¼</p><h3></h3><h3>FAQ:</h3><h4>Q: Is prior programming experience required to enroll in the course?</h4><p>A: While prior programming experience can be beneficial, our course is designed to accommodate learners of all backgrounds. We provide comprehensive tutorials and resources to help you grasp the fundamentals of programming and get started with Machine Learning.</p><h4>Q: How long does it take to complete the course?</h4><p>A: The duration of the course varies depending on your pace and level of commitment. On average, most learners complete the course within 3 to 6 months. However, you have the flexibility to study at your own pace and revisit materials as needed.</p><h4>Q: Are there any prerequisites for enrolling in the course?</h4><p>A: While there are no strict prerequisites, familiarity with basic mathematics, statistics, and programming concepts can be advantageous. We provide supplementary materials and support to help you build the necessary foundation for success in the course.</p><h4>Q: Will I receive a certificate upon completion of the course?</h4><p>A: Yes, upon successfully completing the course requirements, you'll receive a certificate of completion that validates your proficiency in Machine Learning concepts and techniques. This certificate can enhance your credentials and demonstrate your expertise to potential employers.</p><h4>Q: How does the course structure accommodate working professionals?</h4><p>A: Our course offers flexible scheduling options, allowing you to balance your studies with your professional and personal commitments. With on-demand access to course materials and resources, you can learn at your own convenience and progress at a pace that suits your lifestyle.</p><p>Don't miss out on the opportunity to unlock your full potential with our Machine Learning course! Enroll today and embark on a transformative journey that will shape the future of your career. ðâ¨</p></div></div><div><div><div><h3>
Course Curriculum</h3><div><div><h4>
Module 1_ Introduction to Machine Learning</h4><div><div><ul><li><div>
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Introduction to Machine Learning</h5></div><div>
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Module 2_ Linear Regression</h4><div><div><ul><li><div>
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Linear Regression</h5></div><div>
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Module 3_ Logistic Regression</h4><div><div><ul><li><div>
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Module 4_ Decision Trees and Random Forests</h4><div><div><ul><li><div>
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Module 5_ Support Vector Machines (SVMs)</h4><div><div><ul><li><div>
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Support Vector Machines (SVMs)</h5></div><div>
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Module 6_ k-Nearest Neighbors (k-NN)</h4><div><div><ul><li><div>
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k-Nearest Neighbors (k-NN)</h5></div><div>
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Module 7_ Naive Bayes</h4><div><div><ul><li><div>
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Naive Bayes</h5></div><div>
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Module 8_ Clustering</h4><div><div><ul><li><div>
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Clustering</h5></div><div>
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Module 9_ Dimensionality Reduction</h4><div><div><ul><li><div>
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Dimensionality Reduction</h5></div><div>
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Module 10_ Neural Networks</h4><div><div><ul><li><div>
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Neural Networks</h5></div><div>
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Compete High

Machine Learning

<p><strong>***24 Hour Limited Time Flash Sale*** <br /><br /> Data Science &amp; Machine Learning, Excel Pivot &amp; Machine Learning with Python Admission Gifts  <br /><br /> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter</strong></p><p>Immerse yourself in the world of Data Science, Machine Learning and Python with our exclusive bundle! Presenting eight thoughtfully curated courses, this bundle aims to enhance your understanding of intricate concepts. Within this collection, we proudly offer three QLS-endorsed courses: "2021 Data Science &amp; Machine Learning with R from A-Z", "Excel Pivot Tables, Pivot Charts, Slicers, and Timelines", and "Machine Learning with Python", each complemented by a hardcopy certificate upon completion.</p><p>Additionally, delve deeper with our five relevant CPD QS accredited courses. Explore Python Data Science with Numpy, Pandas, and Matplotlib. Uncover the secrets of R Programming for Data Science, enhance your statistical prowess with Statistics &amp; Probability for Data Science &amp; Machine Learning, and master spatial visualisation in Python. To top it all, there's a course on Google Data Studio for Data Analytics.</p><p><strong>Key Features of the Data Science &amp; Machine Learning, Excel Pivot &amp; Machine Learning with Python Bundle:</strong></p><ul><li><strong>3 QLS-Endorsed Courses:</strong> We proudly offer 3 QLS-endorsed courses within our Data Science &amp; Machine Learning, Excel Pivot &amp; Machine Learning with Python bundle, providing you with industry-recognized qualifications. Plus, you'll receive a <strong>free hardcopy certificate</strong> for each of these courses.</li><ul><li><strong>QLS Course 01: 2021 Data Science &amp; Machine Learning with R from A-Z</strong></li><li><strong> QLS Course 02: Excel Pivot Tables, Pivot Charts, Slicers, and Timelines</strong></li><li><strong> QLS Course 03: Machine Learning with Python</strong></li></ul></ul><ul><li><strong>5 CPD QS Accredited Courses</strong>: Additionally, our bundle includes 5 relevant CPD QS accredited courses, ensuring that you stay up-to-date with the latest industry standards and practices.</li><ul><li><strong>Course 01:</strong> Python Data Science with Numpy, Pandas and Matplotlib</li><li><strong>Course 02:</strong> R Programming for Data Science</li><li><strong>Course 03:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 04:</strong> Spatial Data Visualisation and Machine Learning in Python</li><li><strong>Course 05:</strong> Google Data Studio: Data Analytics</li></ul></ul><ul><li>In Addition, you'll get <strong>Five Career Boosting Courses</strong> absolutely FREE with this Bundle.</li><ul><li><strong>Course 01:</strong> Professional CV Writing</li><li><strong>Course 02:</strong> Job Search Skills</li><li><strong>Course 03:</strong> Self-Esteem &amp; Confidence Building</li><li><strong>Course 04:</strong> Professional Diploma in Stress Management</li><li><strong>Course 05:</strong> Complete Communication Skills Master Class</li></ul><li>Convenient Online Learning: Our Data Science &amp; Machine Learning, Excel Pivot &amp; Machine Learning with Python courses are accessible online, allowing you to learn at your own pace and from the comfort of your own home.</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Master the usage of Excel Pivot Tables, Pivot Charts, Slicers, and Timelines.</li><li>Develop proficiency in Machine Learning using Python.</li><li>Acquire skills to manipulate data using Numpy, Pandas, and Matplotlib.</li><li>Learn to code in R for Data Science applications.</li><li>Understand the application of Statistics &amp; Probability in Data Science &amp; Machine Learning.</li><li>Learn to create impactful data visualisations and analyse data using Google Data Studio.</li></ul> <p>The "Data Science &amp; Machine Learning, Excel Pivot &amp; Machine Learning with Python" bundle is a comprehensive compilation designed to equip you with the theoretical knowledge necessary for the fast-evolving data-driven world. The three QLS-endorsed courses provide foundational understanding in Data Science, Machine Learning with R, Excel Pivot functionalities, and Machine Learning with Python, thereby setting a strong base.</p><p>Furthermore, the five CPD QS accredited courses offer a deeper dive into the world of Data Science. Whether it is harnessing Python's power for data science tasks, exploring R programming, mastering statistical techniques, understanding spatial data visualisation in Python, or learning to navigate Google Data Studio for Data Analytics, this bundle has you covered. With this comprehensive learning experience, gain the theoretical insight needed to navigate and succeed in the dynamic field of data science.</p> <h2>
        
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                                        250 CPD hours / points
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                                            Accredited by CPD Quality Standards
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                            <ul><li>Individuals interested in theoretical concepts of Data Science and Machine Learning.</li><li>Professionals looking to enhance their knowledge in Excel Pivot Tables and Charts.</li><li>Aspiring data scientists who want to learn Python and R programming for data science.</li><li>Anyone seeking to understand data visualisation and analytics through Python and Google Data Studio.</li></ul>
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                            <ul><li>Data Scientist: Leveraging data for actionable insights (£40,000 - £90,000 per annum).</li><li>Machine Learning Engineer: Designing and implementing machine learning systems (£50,000 - £90,000 per annum).</li><li>Excel Analyst: Using Excel for data analysis and visualisation (£30,000 - £60,000 per annum).</li><li>Python Developer: Developing applications using Python (£40,000 - £80,000 per annum).</li></ul>
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                                    <h4> Digital certificate</h4>
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            Digital certificate -
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        <h4> Hard copy certificate</h4>
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            Hard copy certificate -
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Academy for Health and Fitness

Data Science

Data Skills

Data Science & Machine Learning, Excel Pivot & Machine Learning with Python

<strong>This is an introductory course on machine learning. The course covers a wide range of topics, from handling a dataset to model delivery. Some prior training in Python programming and basic calculus knowledge will help you get the best out of this course.</strong>
        <p>Machine learning is a branch of AI and computer science that focuses on the use of data to imitate the way humans learn and improve its accuracy.

The course is divided into two parts. The first part starts with a brief history of how machine learning started and introduces you to the basics of statistical learning. You will also understand linear regression and classification, which is the logistic regression model. Understand what cross-validation, sampling, and Bootstrap are. Explore how to go beyond linearity; we will specifically look at a couple of interesting examples to improve the linear regression model to see if we can create models that are non-linear.

The second part of the course is completely hands-on labs, which start with an example of predicting fuel efficiency in linear regression. We will then look at a lab on logistic regression with a little bit of mathematics behind it. Understand another lab session on random forests and do a review of decision trees as well. Next, we will look at a lab session on Eigenfaces by using Principle Component Analysis (PCA) and wrap up a course with a lab on ROC-AUC (Receiver Operating Characteristic Curve-Area Under Curve).

By the end of the course, you would have given yourself the skills and confidence to start programming machine learning algorithms.

All resources and code files are placed here: https://github.com/PacktPublishing/Fundamentals-of-Machine-Learning</p>
        <h2>What You Will Learn</h2><p>Learn the basics of statistical learning <br>Understand linear regression, classification, and supervised learning <br>Understand sampling and Bootstrap in machine learning <br>Explore model selection and regularization <br>Understand random forests and decision trees <br>Explore labs on Multilayer Perceptron (MLP)?and RNN</p>
        <h2>Audience</h2><p>This course can be taken by beginners in Python programming, machine learning, and data science. Scientists, data scientists, and data analysts can also opt for this course. The course assumes no prior knowledge. However, some prior training in Python programming and some basic calculus knowledge is helpful for the course.</p>
        <h2>Approach</h2><p>Each topic has its designated video. The video walks through the technical component of a model to prepare students with a mathematical background. <br><br>Each lab session covers one single topic, which will ensure that the topics covered in the course are well understood.</p>
        <h2>Key Features</h2><p>Build customized deep learning models to start your own data science career  * Build customized models to use for different data science projects  * Learn about the fundamental principles of machine learning</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Fundamentals-of-Machine-Learning</p>
        <h2>About the Author</h2><h3>Yiqiao Yin</h3><p>Yiqiao Yin was a PhD student in statistics at Columbia University. He has a BA in mathematics and an MS in finance from the University of Rochester. He also has a wide range of research interests in representation learning: feature learning, deep learning, computer vision, and NLP.

Yiqiao Yin is a senior data scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development. He has held professional positions as an enterprise-level data scientist at EURO STOXX 50 company Bayer, a quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street.</p>
        <h2>Course Outline</h2><h4>1. Lectures</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome</p><p style='margin: 0'>This video explains the contents of the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction</p><p style='margin: 0'>This video explains a brief history of where machine learning started.</p></td></tr><tr><td style='padding: 15px;'><p>3. Basics in Statistical Learning</p><p style='margin: 0'>This video explains some basic notations in statistical learning, such as Xij.</p></td></tr><tr><td style='padding: 15px;'><p>4. Linear Regression</p><p style='margin: 0'>This video explains the foundation of the linear regression model, which is the very first approach to supervised learning.</p></td></tr><tr><td style='padding: 15px;'><p>5. Classification</p><p style='margin: 0'>This video explains one of the most basic forms of classification, which is the logistic regression model.</p></td></tr><tr><td style='padding: 15px;'><p>6. Sampling and Bootstrap</p><p style='margin: 0'>This video explains the two most famous and common procedures-cross-validation and Bootstrap.</p></td></tr><tr><td style='padding: 15px;'><p>7. Model Selection</p><p style='margin: 0'>This video explains model selection and regularization.</p></td></tr><tr><td style='padding: 15px;'><p>8. Going Beyond Linearity</p><p style='margin: 0'>This video explains going beyond linearity; specifically, we will look at a couple of interesting examples to improve the linear regression model to see if we can create models that are non-linear.</p></td></tr><tr><td style='padding: 15px;'><p>9. Tree-Based Methods - Part 1</p><p style='margin: 0'>This part of the video explains decision tree.</p></td></tr><tr><td style='padding: 15px;'><p>10. Tree-Based Methods - Part 2</p><p style='margin: 0'>The second part of the tree-based methods explains random forests.</p></td></tr><tr><td style='padding: 15px;'><p>11. Support Vector Machine (SVM)</p><p style='margin: 0'>This video explains the support vector machine called SVM and its introduction.</p></td></tr><tr><td style='padding: 15px;'><p>12. Deep Learning</p><p style='margin: 0'>This video introduces you to deep learning, artificial neural networks, recurrent neural networks, and more.</p></td></tr><tr><td style='padding: 15px;'><p>13. Unsupervised Learning</p><p style='margin: 0'>This video explains unsupervised learning, Principal Components Analysis (PCA), and clustering.</p></td></tr><tr><td style='padding: 15px;'><p>14. Classification Metrics</p><p style='margin: 0'>This video explains classification metrics and will cover terminologies such as accuracy, specificity, sensitivity, and so on.</p></td></tr></tbody></table><br><h4>2. Labs</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression</p><p style='margin: 0'>This video explains linear regression using an example of predicting fuel efficiency.</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression</p><p style='margin: 0'>This video explains logistic regression with a little bit of mathematics behind it.</p></td></tr><tr><td style='padding: 15px;'><p>3. Ridge</p><p style='margin: 0'>This video explains a lab session on Ridge regression, which holds a unique position in statistical machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Decision Tree</p><p style='margin: 0'>This video explains a lab session on a decision tree, getting dependencies, and how to create mock data.</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>This video explains a lab session on random forests and a review of decision trees.</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Machine (SVM)</p><p style='margin: 0'>This video explains a lab session on Support Vector Machines or SVM.</p></td></tr><tr><td style='padding: 15px;'><p>7. Multilayer Perceptron (MLP)</p><p style='margin: 0'>This video explains a lab session on neural networks and Multilayer Perceptron (MLP) models.</p></td></tr><tr><td style='padding: 15px;'><p>8. CNN</p><p style='margin: 0'>This video continues the lab from neural networks and will have a look at convolutional neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>9. PCA</p><p style='margin: 0'>This video explains a lab session on Eigenfaces using PCA.</p></td></tr><tr><td style='padding: 15px;'><p>10. ROCAUC</p><p style='margin: 0'>In this final lab, you will learn about ROC-AUC (Receiver Operating Characteristic Curve-Area Under Curve).</p></td></tr></tbody></table>

Packt

Python

This is an introductory course on machine learning. The course covers a wide range of topics, from handling a dataset to model delivery. Some prior training in Python programming and basic calculus knowledge will help you get the best out of this course.

Fundamentals of Machine Learning

<p>Are you interested in learning one of the most popular and widely-used programming languages in the world? Look no further than our Python (Machine Learning &amp; Programming) bundle, endorsed by the Quality Licence Scheme. This comprehensive bundle is designed to provide you with a solid foundation in Python programming and machine learning, allowing you to pursue a range of exciting career paths in this rapidly-growing field.</p> <p>The Python programming course covers the fundamental concepts of programming in Python, including variables, data types, functions, and control structures. You'll also learn about object-oriented programming, file handling, and exception handling, giving you a broad understanding of how to use Python to solve real-world problems.</p> <p>In addition to Python programming, this bundle also covers machine learning, one of the most in-demand skills in the tech industry. The Machine Learning course provides a comprehensive overview of the concepts and techniques used in machine learning, including regression analysis, decision trees, and clustering. You'll also learn about data pre-processing, model selection, and evaluation, giving you the skills and knowledge needed to build and deploy machine learning models.</p> <p>Upon completing this bundle, you'll receive a hardcopy certificate endorsed by the Quality Licence Scheme, proving your Python programming and machine learning proficiency. So why wait? Enrol today and take the first step towards a rewarding and challenging career in the tech industry.</p> <p><strong>This bundle comprises the following courses:</strong></p> <p><strong>QLS Endorsed Courses:</strong></p> <ul> <li><strong>Course 01:</strong> Certificate in Machine Learning with Python at QLS Level 3</li><li><strong>Course 02:</strong> Diploma in Python Programming at QLS Level 5</li></ul> <p><strong>CPD QS Accredited Courses:</strong></p> <ul> <li><strong>Course 03:</strong> Azure Machine Learning</li><li><strong>Course 04:</strong> Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 05:</strong> Machine Learning Model Using AWS SageMaker Canvas</li><li><strong>Course 06:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 07:</strong> 2021 Data Science &amp; Machine Learning with R from A-Z</li></ul> <p><strong>Learning Outcomes</strong></p> <ul> <li>Develop a solid understanding of Python for building applications and data manipulation.</li><li>Learn the fundamental concepts and techniques used in machine learning and data science using Python.</li><li>Acquire the skills to build and train machine learning models for predictive analysis.</li><li>Understand how to use popular tools and platforms like AWS SageMaker and Azure for machine learning.</li><li>Develop a good understanding of statistics and probability concepts used in data science and machine learning.</li><li>Gain hands-on experience in using R programming language for data science and machine learning.</li><li>Acquire a valuable skill set that can be applied to various industries such as finance, healthcare, and tech.</li></ul> <p><strong>Please Note:</strong> Imperial Academy is a One Education approved resale partner for Quality Licence Scheme Endorsed courses.</p><p>The Machine Learning with Python course is designed to provide learners with a comprehensive understanding of machine learning and data science using Python programming language. This course covers a wide range of topics, including data analysis, data visualisation, regression, classification, clustering, neural networks, and deep learning. Through this course, learners will develop the skills and knowledge to build and train machine learning models for predictive analysis.</p> <p>The Diploma in Python Programming course is designed to teach learners the fundamentals of Python programming language, including syntax, data types, control structures, functions, and modules. Learners will also learn about the different data structures and algorithms used in Python programming. This course provides a strong foundation for learners who are interested in pursuing a career in software development or data science.</p> <p><strong>Certification</strong></p> <p>In pursuing QLS-endorsed courses, learners attain not only profound understanding but also amplify their professional prowess, enriching their curriculum vitae.</p> <p><strong>The Quality Licence Scheme </strong>stands as a reputable and distinguished endorsement, emblematic of exceptional standards and excellence throughout the UK.</p> <p>Investing in this comprehensive package, learners will not only receive one but TWO HARD COPY QLS ENDORSED CERTIFICATES conveniently <strong>delivered to their doorstep!</strong></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>Students and recent graduates looking to enter the field of programming or machine learning</li><li>Professionals looking to enhance their skills in Python programming and machine learning</li><li>Individuals looking for a career change into programming or machine learning</li><li>Anyone interested in learning about Python programming and machine learning</li><li>Entrepreneurs who want to develop their own machine-learning applications</li></ul> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <ul> <li>Python Developer - £35,000 to £65,000 per year</li><li>Machine Learning Engineer - £45,000 to £85,000 per year</li><li>Data Analyst - £25,000 to £50,000 per year</li><li>Software Engineer - £30,000 to £60,000 per year</li><li>Artificial Intelligence Engineer - £50,000 to £90,000 per year</li></ul> <p>Please note that salary ranges are estimates and may vary depending on qualifications, experience and other factors.</p> 
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Imperial Academy

Level 3 & 5 Endorsed Diploma | QLS Hard Copy Certificate Included | Plus 5 CPD Courses | Lifetime Access

Python (Machine Learning & Programming)

<p>In the early days of computing, machine learning and data science were just abstract concepts. Fast forward to the present, and we're living in a world where these technologies are transforming industries and revolutionizing the way we work. Whether you're a business owner looking to gain a competitive edge or a student seeking a career in this exciting field, our courses in Data Science &amp; Machine Learning, Excel Pivot &amp; Machine Learning with Python can help you achieve your goals.</p><p>Our QLS-endorsed courses are designed to equip you with the knowledge and skills you need to thrive in the rapidly evolving world of data science and machine learning. The 2021 Data Science &amp; Machine Learning with R from A-Z course covers everything from data visualization and statistical analysis to machine learning algorithms and predictive modeling. With Excel Pivot Tables, Pivot Charts, Slicers, and Timelines, you'll learn how to leverage the power of Excel to analyze and manipulate data like a pro. And with our Machine Learning with Python course, you'll learn how to create sophisticated models that can make predictions and detect patterns in real-world data.</p><p>In addition to our QLS-endorsed courses, we offer 5 CPD QS accredited relevant courses and 5 CPD QS accredited career development courses. Whether you're looking to expand your knowledge in areas such as statistics or Python programming or you're seeking to enhance your employability through courses in resume writing or interview skills, we have you covered.</p><p>If you're a data enthusiast looking to learn the latest tools and techniques in data science and machine learning, our courses are for you. Whether you're a beginner or an experienced professional, our courses are designed to meet you where you are and help you reach your full potential.</p><p>Enroll today and take the first step towards a fulfilling career in data science and machine learning!</p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01: </strong>Advanced Diploma in Data Science &amp; Machine Learning with R at QLS Level 7</li><li><strong>Course 02: </strong>Excel Pivot Tables for Data Reporting</li><li><strong>Course 03: </strong>Certificate in Machine Learning with Python at QLS Level 3</li></ul><p><strong>CPD QS Accredited Courses:</strong></p><ul><li><strong>Course 04:</strong> Python Data Science with Numpy, Pandas and Matplotlib</li><li><strong>Course 05:</strong> R Programming for Data Science</li><li><strong>Course 06:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 07:</strong> Spatial Data Visualisation and Machine Learning in Python</li><li><strong>Course 08:</strong> Google Data Studio: Data Analytics</li></ul><p>Take your career to the next level with our bundle that includes not only technical courses but also <strong>five guided courses</strong> focused on personal development and <strong> career growth.</strong></p><ul><li><strong>Course 09: </strong>Leadership and Management Course</li><li><strong>Course 10:</strong> Career Development and Passion</li><li><strong>Course 11: </strong>Professional CV Writing</li><li><strong>Course 12: </strong>Boost Your Confidence and Self-Esteem</li><li><strong>Course 13: </strong>Public Speaking: Confident Delivery Skills</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Master data analysis and visualization techniques using R, Excel, and Python</li><li>Learn to build and implement machine learning algorithms</li><li>Understand how to work with pivot tables, pivot charts, and slicers in Excel</li><li>Develop a strong understanding of statistics and probability for data science and machine learning</li><li>Gain expertise in spatial data visualization and machine learning in Python</li><li>Learn how to use Numpy, Pandas, and Matplotlib for data analysis with Python</li><li>Become proficient in R programming for data science and machine learning</li></ul><p><strong>Data Science &amp; Machine Learning with R:</strong> This course provides a comprehensive overview of data science and machine learning, starting from the basics and progressing to advanced techniques. Learners will gain an understanding of the data science and machine learning marketplace, job opportunities, and different algorithms used in the field. This course is perfect for individuals who want to pursue a career in data science or machine learning.</p><p><strong>Excel Pivot Tables, Pivot Charts, Slicers, and Timelines</strong>: This course teaches learners how to work with pivot tables, pivot charts, and slicers in Excel. Learners will develop a strong understanding of how to create and format pivot tables, sort and filter data, and create a dashboard using slicers and timelines. This course is ideal for anyone who works with data in Excel, including analysts, accountants, and business professionals.</p><p><strong>Machine Learning with Python:</strong> This course covers the basics of machine learning algorithms and provides learners with the skills needed to build and implement these algorithms in Python. Learners will explore regression and classification techniques, as well as the preprocessing steps needed to prepare data for machine learning. This course is ideal for individuals who want to gain expertise in machine learning using Python.</p><p><strong>Certification</strong></p><p>Learners will qualify for the <strong>highly sought-after QLS Endorsed Certificate</strong> after successfully passing the assessment at the conclusion of each endorsed course.</p><p><strong>The Quality Licence Scheme</strong> is a credible and renowned endorsement that marks <strong>high quality</strong><strong>and excellence</strong> in the UK. It is well accepted and sought after by industry experts and recruiters.</p><p>Learners who purchase this bundle will receive <strong>Three Hardcopy QLS Certificates at their door!</strong></p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        35 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 science or machine learning</li><li>Analysts, accountants, and business professionals who work with data in Excel</li><li>Individuals looking to gain expertise in machine learning using Python</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £24,000 - £40,000 per year</li><li>Data Scientist: £35,000 - £80,000 per year</li><li>Machine Learning Engineer: £40,000 - £80,000 per year</li><li>Business Intelligence Analyst: £27,000 - £50,000 per year</li><li>Data Engineer: £30,000 - £65,000 per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

One Education

Data Science & Machine Learning, Excel Pivot & Machine Learning with Python Course

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			<h2>Description </h2>		</div>
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				<p>Register on the Machine Learning Model Using AWS SageMaker Canvas today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career.</p><p>The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials.</p><p>Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a certificate as proof of your course completion.</p><p>The Machine Learning Model Using AWS SageMaker Canvas course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablets, and smartphones.</p><p>The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly!</p>					</div>
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			<h2>What You Get With This Course</h2>		</div>
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										<span>Receive a digital certificate upon successful completion of the course</span>
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										<span>Get taught by experienced, professional instructors</span>
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										<span>Study at a time and pace that suits your learning style</span>
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										<span>Get instant feedback on assessments</span>
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										<span>24/7 help and advice via email or live chat</span>
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										<span>Get full tutor support on weekdays (Monday to Friday)</span>
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			<h2>Certificate of Achievement </h2>		</div>
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				<p><span>After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post. </span></p>					</div>
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				<p><span>You need to attend an assessment right after the completion of this course to evaluate your progression. For passing the assessment, you need to score at least 60%. After submitting your assessment, you will get feedback from our experts immediately.</span></p>					</div>
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			<h2>Who Is This Course For</h2>		</div>
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				<p>The course is ideal for those who already work in this sector or are aspiring professionals. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.</p><p><span> </span></p>					</div>
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				<h3>Course Content</h3>

<div id="course_curriculum">
<table width="100%">				<tbody><tr>
					<td colspan="4">Introduction To Machine Learning</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>What is Machine Learning?</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Introduction To AWS</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>What is Amazon Web Services (AWS)?</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Signing into AWS Console</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Introduction To SageMaker</td>
				</tr>
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					<td><i></i></td>
					<td>What is SageMaker, and how it is used for Machine Learning?</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>What is SageMaker Canvas?</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
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					<td colspan="4">Setup</td>
				</tr>
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					<td><i></i></td>
					<td>SageMaker Domain and User Setup</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>Setup Data in S3 Buckets for use in SageMaker</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">SageMaker Canvas Interface Walkthrough</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Navigating in SageMaker Canvas</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
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					<td colspan="4">Project 01: Banknote Authentication</td>
				</tr>
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					<td><i></i></td>
					<td>Adding Training Data</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>Building and Using Model for Prediction</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predict Single &amp; Batch Dataset</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>Validating Accuracy of Batch Predictions</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
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					<td colspan="4">Project 02: Spam SMS Detection</td>
				</tr>
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					<td><i></i></td>
					<td>Adding Train &amp; Test Data</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
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					<td><i></i></td>
					<td>Building and Using Model for Prediction</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
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					<td><i></i></td>
					<td>Predicting Data and Validating Accuracy</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
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					<td colspan="4">Project 03: Customer Churn Prediction</td>
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					<td><i></i></td>
					<td>Adding Data</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
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					<td><span><i></i> 00:03:00</span></td>
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					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
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					<td colspan="4">Project 04: Wine Quality Prediction</td>
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					<td><i></i></td>
					<td>Adding &amp; Joining Datasets</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
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					<td>White Wine Quality Prediction</td>
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					<td colspan="4">Other Important Features In SageMaker Canvas</td>
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					<td colspan="4">Congratulations &amp; Next Steps</td>
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					<td colspan="4">Order your Certificates &amp; Transcripts</td>
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					<td>Order your Certificates &amp; Transcripts</td>
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			</div>
			<h2>Frequently Asked Questions</h2>
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                                    Are there any prerequisites for taking the course?
                                </span>
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                                        There are no specific prerequisites for this course, nor
                                        are there any formal entry requirements. All you need is
                                        an internet connection, a good understanding of English
                                        and a passion for learning for this course.
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                                <span>
                                    Can I access the course at any time, or is there a set
                                    schedule?
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                                        You have the flexibility to access the course at any time
                                        that suits your schedule. Our courses are self-paced,
                                        allowing you to study at your own pace and convenience.
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                                <span>
                                    How long will I have access to the course?
                                </span>
                                <div>
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                            <div>
                                <div>
                                    <p>
                                        For this course, you will have access to the course
                                        materials for 1 year only. This means you can review the
                                        content as often as you like within the year, even after
                                        you've completed the course. However, if you buy Lifetime
                                        Access for the course, you will be able to access the
                                        course for a lifetime.
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                        <div>
                            <div>
                                <span>
                                    Is there a certificate of completion provided after
                                    completing the course?
                                </span>
                                <div>
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                                    <p>
                                        Yes, upon successfully completing the course, you will
                                        receive a certificate of completion. This certificate can
                                        be a valuable addition to your professional portfolio and
                                        can be shared on your various social networks.
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                                    Can I switch courses or get a refund if I'm not satisfied
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                                    <p>
                                        We want you to have a positive learning experience. If
                                        you're not satisfied with the course, you can request a
                                        course transfer or refund within 14 days of the initial
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                                    How do I track my progress in the course?
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                                        Our platform provides tracking tools and progress
                                        indicators for each course. You can monitor your progress,
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                                    What if I have technical issues or difficulties with the
                                    course?
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                                        If you encounter technical issues or content-related
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                                        available to assist you. You can reach out to them for
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Janets

Azure Machine Learning

Machine Learning Model Using AWS SageMaker Canvas

Duco Digital Training

IT Certifications

IT (Information Technology)

IT Manager

Computing & IT

IT Literacy

The BCS Essentials Certificate in Artificial Intelligence teaches the general principles of Artificial Intelligence, an introduction to Machine Learning and understand it's potential implications and capabilities.

You will learn about human and artificial intelligence, the machine learning process, the different types of agents, the types of machine learning, the benefits, challenges and risks of a machine learning project, ethics in AI and the future of humans and machines in work.

This is a great course for any person or organisation who needs to gain familiarity about Artificial Intelligence and Machine Learning before they commence a project; helping ensure the project approach is correct and avoid the common and costly pitfalls of technology projects.

Artificial Intelligence - BCS Essentials Certificate

<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

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>
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			<h2><b>Course Overview</b></h2>		</div>
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				<div>
				<div>
					<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 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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Machine Learning - Use in Flutter

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

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

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

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

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

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

Deep Learning

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

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