Name: Python for Data Science and Machine Learning
Price: 5 GBP
Availability: InStock

<strong>This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.</strong>
        <p>Machine learning is the key to development in many areas, such as IT, security, marketing, automation, and even medicine. Without machine learning, it is impossible to build intelligent applications and devices, such as Alexa, Siri, and Google Assistant. This course will help to get familiar with data science and machine learning.

The course starts with an introduction to data science, explaining different terms associated with it. You will also become familiar with machine learning and data science modeling and explore the key differences between model parameters and hyperparameters. Next, you will become familiar with the concepts of machine learning models, such as linear regression, decision trees, random forests, neural networks, and clustering techniques. Towards the end, you will learn how to evaluate machine learning models and learn the best practices to succeed in your data scientist role.

By the end of this course, you will have a solid understanding of data science and machine learning fundamentals.</p>
        <h2>What You Will Learn</h2><p>Become familiar with data science and machine learning terms<br>Distinguish between model parameters and hyperparameters<br>Distinguish between supervised and unsupervised learning<br>Discover how decision trees, bagging, and random forest works<br>Understand the importance of the k-nearest neighbors (KNN) algorithm in machine learning<br>Learn about neural networks and clustering techniques<br>Evaluate the performance of machine learning models</p>
        <h2>Audience</h2><p>This course is designed for students and beginners who want to understand the concepts, statistics, and math behind machine learning algorithms and for those who are curious to solve real-world problems using machine learning and data science. Everything is taught from scratch; hence, there are no prerequisites to get started with this course.</p>
        <h2>Approach</h2><p>With the help of simple explanation, avoiding confusing mathematical notation and jargon, this course covers the fundamentals of data science, machine learning, and data mining from scratch.</p>
        <h2>Key Features</h2><p>Learn the fundamentals of data science, machine learning, and data mining * Learn interesting techniques to evaluate a machine learning model * Discover the best practices to solve real-world problems using machine learning</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. Welcome and Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome</p><p style='margin: 0'>This video welcomes you to the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Data Science</p><p style='margin: 0'>This video provides an introduction to data science.</p></td></tr></tbody></table><br><h4>2. Preliminary Requirements to Understand Data Science and Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Understanding Data Science Terms</p><p style='margin: 0'>This video provides an overview of the terms used in data science.</p></td></tr><tr><td style='padding: 15px;'><p>2. Understanding Machine Learning</p><p style='margin: 0'>This video presents an overview of machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>3. Types of Learning</p><p style='margin: 0'>This video explains the different types of learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Understanding Data Science Modeling</p><p style='margin: 0'>This video focuses on data science modeling.</p></td></tr><tr><td style='padding: 15px;'><p>5. Model parameters versus Hyperparameters</p><p style='margin: 0'>This video explains the difference between model parameters and hyperparameters.</p></td></tr></tbody></table><br><h4>3. Machine Learning Models</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How Linear Regression Works</p><p style='margin: 0'>This video demonstrates how linear regression works.</p></td></tr><tr><td style='padding: 15px;'><p>2. How Decision Trees Work</p><p style='margin: 0'>This video demonstrates how decision trees work.</p></td></tr><tr><td style='padding: 15px;'><p>3. How Bagging and Random Forest Work</p><p style='margin: 0'>This video demonstrates the working of bagging and random forest.</p></td></tr><tr><td style='padding: 15px;'><p>4. How Support Vector Machine Works</p><p style='margin: 0'>This video demonstrates the working of support vector machines.</p></td></tr><tr><td style='padding: 15px;'><p>5. Neural Networks- Part 1</p><p style='margin: 0'>This video is the first part of a two-part video that explains neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>6. Neural Networks- Part 2</p><p style='margin: 0'>This is the second part of the two-part video that explains neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>7. How Logistic Regression Works</p><p style='margin: 0'>This video demonstrates the working of logistic regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. How k-nearest neighbour (KNN) Works</p><p style='margin: 0'>This video focuses on KNN and explains how it works.</p></td></tr><tr><td style='padding: 15px;'><p>9. Clustering Techniques</p><p style='margin: 0'>This video explains the different clustering techniques.</p></td></tr></tbody></table><br><h4>4. Model Performance</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Evaluating Models Performance</p><p style='margin: 0'>This video demonstrates how to evaluate the performance of the models.</p></td></tr></tbody></table><br><h4>5. Best Practices for a Data Scientist</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Best Practices for a Data Scientist</p><p style='margin: 0'>This video explains the best practices used by data scientists.</p></td></tr></tbody></table>

Packt

Data Science

Algorithms

This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.

Mastering Data Science and Machine Learning Fundamentals

General Admission

<strong>This course will be mainly focusing on machine learning algorithms. Throughout this course, we are preparing our machine to make it ready for a prediction test.</strong>
        <p>Artificial intelligence, machine learning, and deep learning neural networks are the most used terms in the technology world today. They're also the most misunderstood and confused terms. Artificial intelligence is a broad spectrum of science that tries to make machines intelligent like humans, while machine learning and neural networks are two subsets that sit within this vast machine learning platform. But in this course, you will focus mainly on machine learning, which will include preparing your machine to make it ready for a prediction test.
You will be using Python as your programming language. Python is a great tool for the development of programs that perform data analysis and prediction. It has a variety of classes and features that perform complex mathematical analyses and provide solutions in just a few lines of code, making it easier for you to get up to speed with data science and machine learning.

Machine learning and data science jobs are among the most lucrative in the technology industry in recent times. Exploring this course will help you get well-versed with essential concepts and prepare you for a career in these fields.

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-and-Data-Science-with-Python-A-Complete-Beginners-Guide</p>
        <h2>What You Will Learn</h2><p>Install Python and required libraries<br>Choose the best machine learning model<br>Automate and combine workflows with pipeline<br>Look at performance improvement with ensembles<br>Study performance improvement with algorithm parameter tuning<br>Finalize a machine learning project</p>
        <h2>Audience</h2><p>This course is for beginners who are interested in machine learning using Python.</p>
        <h2>Approach</h2><p>An exhaustive course packed with step-by-step instructions, working examples, and helpful advice. This course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest.</p>
        <h2>Key Features</h2><p>Learn machine learning and data science using Python * A practical course designed for beginners who are interested in machine learning using Python * Work on predictions and case studies</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-and-data-science-with-python-a-complete-beginners-guide</p>
        <h2>About the Author</h2><h3>Abhilash Nelson </h3><p>Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than eight years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.</p>
        <h2>Course Outline</h2><h4>1. Course Overview and Table of Contents</h4><p></p><br><h4>2. Introduction to Machine Learning</h4><p></p><br><h4>3. System and Environment Preparation</h4><p></p><br><h4>4. Learn Basics of Python</h4><p></p><br><h4>5. Learn Basics of NumPy</h4><p></p><br><h4>6. Learn Basics of Matplotlib</h4><p></p><br><h4>7. Learn Basics of Pandas</h4><p></p><br><h4>8. Understanding the CSV Data File</h4><p></p><br><h4>9. Load and Read CSV Data File</h4><p></p><br><h4>10. Dataset Summary</h4><p></p><br><h4>11. Dataset Visualization</h4><p></p><br><h4>12. Data Preparation</h4><p></p><br><h4>13. Feature Selection</h4><p></p><br><h4>14. Refresher Session - the Mechanism of Re-Sampling, Training, and Testing</h4><p></p><br><h4>15. Algorithm Evaluation Techniques</h4><p></p><br><h4>16. Algorithm Evaluation Metrics</h4><p></p><br><h4>17. Classification Algorithm Spot Check - Logistic Regression</h4><p></p><br><h4>18. Classification Algorithm Spot Check - Linear Discriminant Analysis</h4><p></p><br><h4>19. Classification Algorithm Spot Check - K-Nearest Neighbors</h4><p></p><br><h4>20. Classification Algorithm Spot Check - Naive Bayes</h4><p></p><br><h4>21. Classification Algorithm Spot Check - CART</h4><p></p><br><h4>22. Classification Algorithm Spot Check - Support Vector Machines</h4><p></p><br><h4>23. Regression Algorithm Spot Check - Linear Regression</h4><p></p><br><h4>24. Regression Algorithm Spot Check - Ridge Regression</h4><p></p><br><h4>25. Regression Algorithm Spot Check - LASSO Linear Regression</h4><p></p><br><h4>26. Regression Algorithm Spot Check - Elastic Net Regression</h4><p></p><br><h4>27. Regression Algorithm Spot Check - K-Nearest Neighbors</h4><p></p><br><h4>28. Regression Algorithm Spot Check - CART</h4><p></p><br><h4>29. Regression Algorithm Spot Check - Support Vector Machines (SVM)</h4><p></p><br><h4>30. Compare Algorithms - Part 1: Choosing the Best Machine Learning Model</h4><p></p><br><h4>31. Compare Algorithms - Part 2: Choosing the Best Machine Learning Model</h4><p></p><br><h4>32. Pipelines: Data Preparation and Data Modelling</h4><p></p><br><h4>33. Pipelines: Feature Selection and Data Modelling</h4><p></p><br><h4>34. Performance Improvement: Ensembles - Voting</h4><p></p><br><h4>35. Performance Improvement: Ensembles - Bagging</h4><p></p><br><h4>36. Performance Improvement: Ensembles - Boosting</h4><p></p><br><h4>37. Performance Improvement: Parameter Tuning Using Grid Search</h4><p></p><br><h4>38. Performance Improvement: Parameter Tuning Using Random Search</h4><p></p><br><h4>39. Export, Save and Load Machine Learning Models: Pickle</h4><p></p><br><h4>40. Export, Save and Load Machine Learning Models: Joblib</h4><p></p><br><h4>41. Finalizing a Model - Introduction and Steps</h4><p></p><br><h4>42. Finalizing a Classification Model - the Pima Indian Diabetes Dataset</h4><p></p><br><h4>43. Quick Session: Imbalanced Dataset - Issue Overview and Steps</h4><p></p><br><h4>44. Iris Dataset: Finalizing Multi-Class Dataset</h4><p></p><br><h4>45. Finalizing a Regression Model - the Boston Housing Price Dataset</h4><p></p><br><h4>46. Real-Time Predictions: Using the Pima Indian Diabetes Classification Model</h4><p></p><br><h4>47. Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset</h4><p></p><br><h4>48. Real-Time Predictions: Using the Boston Housing Regression Model</h4><p></p>

Python

This course will be mainly focusing on machine learning algorithms. Throughout this course, we are preparing our machine to make it ready for a prediction test.

Machine Learning and Data Science with Python: A Complete Beginners Guide

<strong>The goal of this course is to teach you how to deploy machine learning models in Flask. You'll also learn to deploy applications to the web. You'll learn to train your models, test them, and ensure that they are of the best quality.</strong>
        <p>Flask is a web application framework used to develop web applications. Getting started with Flask is easy, and its power lies in its ability to scale up to complex applications. In this course, you'll learn the most effective ways to use Flask in order to create your own web application.

You'll begin with an introduction to Flask and quickly dive into defining and training your model. You'll perform various actions on this model to train it and ensure that it is of the best quality for your application. You'll also create and test API endpoints so you can predict the model's behavior over time.

By the end of this course, you'll have the confidence to deploy this application to the web and learn how to fix any errors that may arise during this process.

The code bundle for this course is available at https://github.com/PacktPublishing/Deploying-Machine-Learning-Models-with-Flask-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Define and train your model<br>Test the model availability and make predictions<br>Create a Flask endpoint and perform quality checks<br>Use image classification in your Flask application<br>Predict images on the Flask application<br>Deploy your machine learning model to the web</p>
        <h2>Audience</h2><p>This course is for developers who are looking to learn Flask. A basic understanding of Python is preferred here as this will help you grasp the Flask concepts easier. You also need to have a functioning knowledge of machine learning and neural networks.</p>
        <h2>Approach</h2><p>This course takes a hands-on approach where the author implements the code and explains why it is done.</p>
        <h2>Key Features</h2><p>Create your own API endpoint for your machine learning models * Create a Flask application from scratch * Deploy your model on the web</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Deploying-Machine-Learning-Models-with-Flask-for-Beginners</p>
        <h2>About the Author</h2><h3>Dan We</h3><p>Daniel Weikert is a 33-year-old entrepreneur, data enthusiast, consultant, and trainer. He is a master's degree holder certified in Power BI, Tableau, Alteryx (Core and Advanced), and KNIME (L1-L3).
He is currently working in the business intelligence field and helps companies and individuals obtain vital insights from their data to deliver long-term strategic growth and outpace their competitors.

He has a passion for learning and teaching. He is committed to supporting other people by offering them educational services and helping them accomplish their goals, gain expertise in their profession, or explore new careers.</p>
        <h2>Course Outline</h2><h4>1. Deploying machine learning models with flask for beginners - quick and easy</h4><p></p><br><h4>2. BONUS SECTION How to deploy your  ML Models to the web for free</h4><p></p>

Flask

The goal of this course is to teach you how to deploy machine learning models in Flask. You'll also learn to deploy applications to the web. You'll learn to train your models, test them, and ensure that they are of the best quality.

Deploying Machine Learning Models with Flask for Beginners

Janets

Learning & Development

Online Course Marketing

Managed Learning Programme (MLP)

Machine Learning (ML)

Learning To Learn

Trainers Course

Machine Learning Basics

<p>If you aim to enhance your Machine Learning &amp; Artificial Intelligence skills, our comprehensive Machine Learning &amp; Artificial Intelligence course is perfect for you. Designed for success, this Machine Learning &amp; Artificial Intelligence course covers everything from basics to advanced topics in Machine Learning &amp; Artificial Intelligence.</p><p>Each lesson in this Machine Learning &amp; Artificial Intelligence course is crafted for easy understanding, enabling you to become proficient in Machine Learning &amp; Artificial Intelligence. Whether you are a beginner or looking to sharpen your existing skills, this Machine Learning &amp; Artificial Intelligence is the ideal choice.</p><p>With our Machine Learning &amp; Artificial Intelligence exclusive bundle, you will get<strong> a PDF Certificate, PDF Transcript and Digital Student ID Card (worth £50) Absolutely FREE. </strong></p><p><strong>Courses are Included in This Bundle:</strong></p><ul><li><strong>Course 01: </strong>Machine Learning Basics</li><li><strong>Course 02: </strong>Hands-on Machine Learning Project - Auto Image Captioning for Social Media</li><li><strong>Course 03: </strong>Machine Learning with Python Course</li><li><strong>Course 04: </strong>Project on Deep Learning - Artificial Neural Network</li><li><strong>Course 05: </strong>Data Science &amp; Machine Learning with R Training</li><li><strong>Course 06: </strong>ChatGPT for Marketing and Productivity with AI Tools</li></ul><p><strong>Why Choose Our Course?</strong></p><ul><li>FREE Machine Learning &amp; Artificial Intelligence certificate accredited</li><li>Get a free student ID card with Machine Learning &amp; Artificial Intelligence Training</li><li>Get instant access to this Machine Learning &amp; Artificial Intelligence course.</li><li>Learn Machine Learning &amp; Artificial Intelligence from anywhere in the world</li><li>Machine Learning &amp; Artificial Intelligence is affordable and simple to understand</li><li>This bundle is an entirely online, interactive lesson with voiceover audio</li><li>Lifetime access to the Machine Learning &amp; Artificial Intelligence course materials</li><li>The Machine Learning &amp; Artificial Intelligence comes with 24/7 tutor support</li></ul><p>So enrol now in this Machine Learning &amp; Artificial Intelligence Today to advance your career!</p> <p>Start your learning journey straightaway!</p><p>This Machine Learning &amp; Artificial Intelligence's curriculum has been designed by Machine Learning &amp; Artificial Intelligence experts with years of Machine Learning &amp; Artificial Intelligence experience behind them. The Machine Learning &amp; Artificial Intelligence course is extremely dynamic and well-paced to help you understand Machine Learning &amp; Artificial Intelligence with ease. You'll discover how to master Machine Learning &amp; Artificial Intelligence skills while exploring relevant and essential topics.</p><p><strong>Assessment Process</strong></p><p>Once you have completed all the courses in the Machine Learning &amp; Artificial Intelligence bundle, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>This Machine Learning &amp; Artificial Intelligence bundle is suitable for everyone.</p>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You will not need any prior background or expertise.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This Machine Learning &amp; Artificial Intelligence bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited  Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            £29
        </p>
            <div><p>If you are an international student, you will be required to pay an additional fee of <strong>10 GBP</strong> for international delivery, and <strong>4.99 GBP</strong> for delivery within the UK, for each certificate</p></div>

                        </div>

Academy for Health and Fitness

Azure Machine Learning

ICDL (International Certification of Digital Literacy)

Machine Learning & Artificial Intelligence

<strong>Learn complete hands-on Regression analysis for practical Statistical modelling and Machine Learning in R</strong>
        <p>With so many R Statistics and Machine Learning courses around, why enroll for this?

Regression analysis is one of the central aspects of both Statistics and Machine Learning based analysis. This course will teach you Regression analysis for both Statistical data analysis and ML in R. It explores relevant concepts in a practical way, from basic to expert level. This course can help you achieve better grades, gain new analysis tools for your academic career, implement your knowledge in a work setting, and make business forecasting-related decisions. You will go all the way from implementing and inferring simple OLS (Ordinary Least Square) regression models to dealing with issues of multicollinearity in regression to ML based regression models.

Become a Regression analysis expert and harness the power of R for your analysis

&bull; Get started with R and RStudio. Install these on your system, learn to load packages, and read in different types of data in R
&bull; Carry out data cleaning and data visualization using R
&bull; Implement Ordinary Least Square (OLS) regression in R and learn how to interpret the results.
&bull; Learn how to deal with multicollinearity both through the variable selection and regularization techniques such as ridge regression
&bull; Carry out variable and regression model selection using both statistical and machine learning techniques, including using cross-validation methods.
&bull; Evaluate the regression model accuracy
&bull; Implement Generalized Linear Models (GLMs) such as logistic regression and Poisson regression. Use logistic regression as a binary classifier to distinguish between male and female voices.
&bull; Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data.
&bull; Work with tree-based ML models

All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Regression-Analysis-for-Statistics-and-Machine-Learning-in-R</p>
        <h2>What You Will Learn</h2><p>Implement and infer Ordinary Least Square (OLS) regression using R<br>Apply statistical and ML based regression models to deal with problems such as multicollinearity<br>Carry out the variable selection and assess model accuracy using techniques such as cross-validation<br>Implement and infer Generalized Linear Models (GLMs), including using logistic regression as a binary classifier</p>
        <h2>Audience</h2><p>This course is for students interested in getting started with data science applications in the RStudio environment. Students wishing to learn to implement statistical modeling techniques for regression analysis on real data. Anyone with prior exposure to R who wants to get started with practical data science.</p>
        <h2>Approach</h2><p>This is a practical, hands-on course. We will spend some time dealing with some theoretical concepts related to both statistical and machine learning regression analysis. However, the majority of the course will focus on implementing different techniques on real data and interpreting the results. After each video, you will learn a new concept or technique which you can apply to your own projects.</p>
        <h2>Key Features</h2><p>Provided practical indepth training for you to get started with R * Perform some of the most common advanced regression analysis-based techniques * Use R to perform different statistical and machine learning data analysis and visualization tasks</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/regression-analysis-for-statistics-and-machine-learning-in-r</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. Get Started with Practical Regression Analysis in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools</p></td></tr><tr><td style='padding: 15px;'><p>2. Difference Between Statistical Analysis & Machine Learning</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Difference Between Statistical Analysis &amp; Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Getting Started with R and R Studio</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Getting Started with R and R Studio</p></td></tr><tr><td style='padding: 15px;'><p>4. Reading in Data with R</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Reading in Data with R</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Cleaning with R</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Data Cleaning with R</p></td></tr><tr><td style='padding: 15px;'><p>6. Some More Data Cleaning with R</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Some More Data Cleaning with R</p></td></tr><tr><td style='padding: 15px;'><p>7. Basic Exploratory Data Analysis in R</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Basic Exploratory Data Analysis in R</p></td></tr><tr><td style='padding: 15px;'><p>8. Conclusion to Section 1</p><p style='margin: 0'>Get Started with Practical Regression Analysis in R: Conclusion to Section 1</p></td></tr></tbody></table><br><h4>2. Ordinary Least Square Regression Modelling</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. OLS Regression- Theory</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: OLS Regression- Theory</p></td></tr><tr><td style='padding: 15px;'><p>2. OLS-Implementation</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: OLS-Implementation</p></td></tr><tr><td style='padding: 15px;'><p>3. More on Result Interpretations</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: More on Result Interpretations</p></td></tr><tr><td style='padding: 15px;'><p>4. Confidence Interval-Theory</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Confidence Interval-Theory</p></td></tr><tr><td style='padding: 15px;'><p>5. Calculate the Confidence Interval in R</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Calculate the Confidence Interval in R</p></td></tr><tr><td style='padding: 15px;'><p>6. Confidence Interval and OLS Regressions</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Confidence Interval and OLS Regressions</p></td></tr><tr><td style='padding: 15px;'><p>7. Linear Regression without Intercept</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Linear Regression without Intercept</p></td></tr><tr><td style='padding: 15px;'><p>8. Implement ANOVA on OLS Regression</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Implement ANOVA on OLS Regression</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Linear Regression</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Multiple Linear Regression</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Linear regression with Interaction and Dummy Variables</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Multiple Linear regression with Interaction and Dummy Variables</p></td></tr><tr><td style='padding: 15px;'><p>11. Some Basic Conditions that OLS Models Have to Fulfill</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Some Basic Conditions that OLS Models Have to Fulfill</p></td></tr><tr><td style='padding: 15px;'><p>12. Conclusions to Section 2</p><p style='margin: 0'>Ordinary Least Square Regression Modelling: Conclusions to Section 2</p></td></tr></tbody></table><br><h4>3. Deal with Multicollinearity in OLS Regression Models</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Identify Multicollinearity</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: Identify Multicollinearity</p></td></tr><tr><td style='padding: 15px;'><p>2. Doing Regression Analyses with Correlated Predictor Variables</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: Doing Regression Analyses with Correlated Predictor Variables</p></td></tr><tr><td style='padding: 15px;'><p>3. Principal Component Regression in R</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: Principal Component Regression in R</p></td></tr><tr><td style='padding: 15px;'><p>4. Partial Least Square Regression in R</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: Partial Least Square Regression in R</p></td></tr><tr><td style='padding: 15px;'><p>5. Ridge Regression in R</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: Ridge Regression in R</p></td></tr><tr><td style='padding: 15px;'><p>6. LASSO Regression</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: LASSO Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Conclusion to Section 3</p><p style='margin: 0'>Deal with Multicollinearity in OLS Regression Models: Conclusion to Section 3</p></td></tr></tbody></table><br><h4>4. Variable & Model Selection</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Why Do Any Kind of Selection?</p><p style='margin: 0'>Variable &amp; Model Selection: Why Do Any Kind of Selection?</p></td></tr><tr><td style='padding: 15px;'><p>2. Select the Most Suitable OLS Regression Model</p><p style='margin: 0'>Variable &amp; Model Selection: Select the Most Suitable OLS Regression Model</p></td></tr><tr><td style='padding: 15px;'><p>3. Select Model Subsets</p><p style='margin: 0'>Variable &amp; Model Selection: Select Model Subsets</p></td></tr><tr><td style='padding: 15px;'><p>4. Machine Learning Perspective on Evaluate Regression Model Accuracy</p><p style='margin: 0'>Variable &amp; Model Selection: Machine Learning Perspective on Evaluate Regression Model Accuracy</p></td></tr><tr><td style='padding: 15px;'><p>5. Evaluate Regression Model Performance</p><p style='margin: 0'>Variable &amp; Model Selection: Evaluate Regression Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>6. LASSO Regression for Variable Selection</p><p style='margin: 0'>Variable &amp; Model Selection: LASSO Regression for Variable Selection</p></td></tr><tr><td style='padding: 15px;'><p>7. Identify the Contribution of Predictors in Explaining the Variation in Y</p><p style='margin: 0'>Variable &amp; Model Selection: Identify the Contribution of Predictors in Explaining the Variation in Y</p></td></tr><tr><td style='padding: 15px;'><p>8. Conclusions to Section 4</p><p style='margin: 0'>Variable &amp; Model Selection: Conclusions to Section 4</p></td></tr></tbody></table><br><h4>5. Dealing with Other Violations of the OLS Regression Models</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Transformations</p><p style='margin: 0'>Dealing with Other Violations of the OLS Regression Models: Data Transformations</p></td></tr><tr><td style='padding: 15px;'><p>2. Robust Regression-Deal with Outliers</p><p style='margin: 0'>Dealing with Other Violations of the OLS Regression Models: Robust Regression-Deal with Outliers</p></td></tr><tr><td style='padding: 15px;'><p>3. Dealing with Heteroscedasticity</p><p style='margin: 0'>Dealing with Other Violations of the OLS Regression Models: Dealing with Heteroscedasticity</p></td></tr><tr><td style='padding: 15px;'><p>4. Conclusions to Section 5</p><p style='margin: 0'>Dealing with Other Violations of the OLS Regression Models: Conclusions to Section 5</p></td></tr></tbody></table><br><h4>6. Generalized Linear Models (GLMs)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What are GLMs?</p><p style='margin: 0'>Generalized Linear Models (GLMs): What are GLMs?</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic regression</p><p style='margin: 0'>Generalized Linear Models (GLMs): Logistic regression</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression for Binary Response Variable</p><p style='margin: 0'>Generalized Linear Models (GLMs): Logistic Regression for Binary Response Variable</p></td></tr><tr><td style='padding: 15px;'><p>4. Multinomial Logistic Regression</p><p style='margin: 0'>Generalized Linear Models (GLMs): Multinomial Logistic Regression</p></td></tr><tr><td style='padding: 15px;'><p>5. Regression for Count Data</p><p style='margin: 0'>Generalized Linear Models (GLMs): Regression for Count Data</p></td></tr><tr><td style='padding: 15px;'><p>6. Goodness of fit testing</p><p style='margin: 0'>Generalized Linear Models (GLMs): Goodness of fit testing</p></td></tr><tr><td style='padding: 15px;'><p>7. Conclusions to Section 6</p><p style='margin: 0'>Generalized Linear Models (GLMs): Conclusions to Section 6</p></td></tr></tbody></table><br><h4>7. Working with Non-Parametric and Non-Linear Data</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Polynomial and Non-linear regression</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Polynomial and Non-linear regression</p></td></tr><tr><td style='padding: 15px;'><p>2. Generalized Additive Models (GAMs) in R</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Generalized Additive Models (GAMs) in R</p></td></tr><tr><td style='padding: 15px;'><p>3. Boosted GAM Regression</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Boosted GAM Regression</p></td></tr><tr><td style='padding: 15px;'><p>4. Multivariate Adaptive Regression Splines (MARS)</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Multivariate Adaptive Regression Splines (MARS)</p></td></tr><tr><td style='padding: 15px;'><p>5. CART-Regression Trees in R</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: CART-Regression Trees in R</p></td></tr><tr><td style='padding: 15px;'><p>6. Conditional Inference Trees</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Conditional Inference Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. Random Forest(RF)</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Random Forest(RF)</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Regression</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Gradient Boosting Regression</p></td></tr><tr><td style='padding: 15px;'><p>9. ML Model Selection</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: ML Model Selection</p></td></tr><tr><td style='padding: 15px;'><p>10. Conclusions to Section 7</p><p style='margin: 0'>Working with Non-Parametric and Non-Linear Data: Conclusions to Section 7</p></td></tr></tbody></table>

Statistics

Programming

Learn complete hands-on Regression analysis for practical Statistical modelling and Machine Learning in R

Regression Analysis for Statistics & Machine Learning in R

<p>Discover the thrilling world of artificial intelligence with the '<strong>Machine Learning Course with Python</strong>'. Immerse yourself in a voyage from foundational concepts, unveiling the mysteries behind algorithms, to diving deep into the cores of preprocessing, regression, and classification. Crafted meticulously, this course introduces Python as the catalyst, opening doors to data-driven decision-making and predictive analysis, empowering your journey in the ever-evolving field of machine learning.</p>
<p><strong>Learning Outcomes</strong></p>
<ul>
<li>Grasp the foundational knowledge of various machine learning algorithms.</li>
<li>Attain proficiency in preprocessing data for optimal outcomes.</li>
<li>Master the nuances of regression analysis using Python.</li>
<li>Delve into the intricacies of classification techniques.</li>
<li>Enhance problem-solving abilities with practical Python-driven machine learning applications.</li>
</ul>
<h3><b>Why choose this Machine Learning Course with Python<span> course</span>?</b></h3>
<ul>
<li>Unlimited access to the course for a lifetime.</li>
<li>Opportunity to earn a certificate accredited by the <b>CPD Quality Standards</b><b> </b>after completing this course.</li>
<li>Structured lesson planning in line with industry standards.</li>
<li>Immerse yourself in innovative and captivating course materials and activities.</li>
<li>Assessments are designed to evaluate advanced cognitive abilities and skill proficiency.</li>
<li>Flexibility to complete the <b>Machine Learning Course with Python</b> Course at your own pace, on your own schedule.</li>
<li>Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience.</li>
</ul>
<h3><b>Who is this Machine Learning Course with Python<span> </span></b><b><span>course </span></b><b>for?</b></h3>
<ul>
<li>Aspiring data scientists eager to harness the power of machine learning.</li>
<li>Python enthusiasts aiming to delve into its applications in AI.</li>
<li>Professionals in the tech industry seeking a transition into data roles.</li>
<li>Academics and researchers wanting to employ machine learning in their work.</li>
<li>Business analysts aiming to leverage predictive analytics for better insights.</li>
</ul>
<h3><b>Career path</b></h3>
<ul>
<li><strong>Data Scientist</strong>: £40,000 - £70,000</li>
<li><strong>Machine Learning Engineer</strong>: £50,000 - £80,000</li>
<li><strong>AI Researcher</strong>: £45,000 - £75,000</li>
<li><strong>Data Analyst</strong>: £30,000 - £50,000</li>
<li><strong>Python Developer</strong>: £35,000 - £65,000</li>
<li><strong>Business Intelligence Developer</strong>: £40,000 - £60,000</li>
</ul>
<h3><b>Prerequisites</b></h3>
<p><span>This </span><b>Machine Learning Course with Python</b><span> does not require you to have any prior qualifications or experience. You can just enrol and start learning.This </span><b>Machine Learning Course with Python</b><span> was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p>
<h3><b>Certification</b></h3>
<p><span>After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8.</span></p><div>
				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4">Module 01: Introduction to Machine Learning Algorithms</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to types of ML algorithm</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 02: Preprocessing</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Importing a dataset in python</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Resolving Missing Values</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Managing Category Variables</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Training and Testing Datasets</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Normalizing Variables</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Normalizing Variables - Python Code</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Summary</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 03: Regression</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Simple Linear Regression - How it works?</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Simple Linear Regreesion - Python Implementation</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Multiple Linear Regression - How it works?</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Multiple Linear Regression - Python Implementation</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Decision Trees - How it works?</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Random Forest - How it works?</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Decision Trees and Random Forest - Python Implementation</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 04: Classification</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>kNN - How it works?</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>kNN - Python Implementation</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Decision Tree Classifier and Random Forest Classifier in Python</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>SVM - How it works?</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>SVM - Python Implementation</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Assignment </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assignment - Machine Learning Course with Python</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Studyhub UK

Machine Learning Course with Python

The purpose of this course is to teach you how to use Python for machine learning to create real-world algorithms. You will gain an in-depth understanding of the fundamentals of deep learning. This course will help you explore different frameworks in Python to solve real-world problems using the core concepts of deep learning and artificial intelligence.

Python for Machine Learning - The Complete Beginner's Course

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			<h2>Overview
</h2>		</div>
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								<div>
					<p><span>This comprehensive course on <b>Machine Learning with Python</b> will deepen your understanding on this topic.</span></p><p><span>After successful completion of this course you can acquire the required skills in this sector. This <b>Machine Learning with Python</b> comes with accredited certification, which will enhance your CV and make you worthy in the job market.</span></p><p><span>So enrol in this course today to fast track your career ladder.</span></p>					</div>
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						<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>
						</div>
				</div>
				<div>
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			<h2>Who is This course for?</h2>		</div>
				</div>
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					<p><span>There is no experience or previous qualifications required for enrolment on this <b>Machine Learning with Python</b>. It is available to all students, of all academic backgrounds.</span></p>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Requirements</h2>		</div>
				</div>
				<div>
				<div>
								<div>
					<p><span>Our <b>Machine Learning with Python</b> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</span></p><p><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></p>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Career Path</h2>		</div>
				</div>
				<div>
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					<p><span>Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business &amp; Management, Admin, Accountancy &amp; Finance, Secretarial &amp; PA, Teaching &amp; Mentoring etc.</span></p>					</div>
						</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
						</div>
						</div>
					</div> <h3>Course Curriculum</h3><div><p>4 sections • 21 lectures • 01:34:00 total length</p></div><div><div><div><div><div><div><div><p>•Introduction to types of ML algorithm: 00:02:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•SVM - Python Implementation: 00:06:00</p></div><div><div><p>•Introduction to types of ML algorithm: 00:02:00</p></div><div><div><p>•Importing a dataset in python: 00:02:00</p></div><div><div><p>•Resolving Missing Values: 00:06:00</p></div><div><div><p>•Managing Category Variables: 00:04:00</p></div><div><div><p>•Training and Testing Datasets: 00:07:00</p></div><div><div><p>•Normalizing Variables: 00:02:00</p></div><div><div><p>•Normalizing Variables - Python Code: 00:03:00</p></div><div><div><p>•Summary: 00:01:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Simple Linear Regression - How it works?: 00:04:00</p></div><div><div><p>•Simple Linear Regreesion - Python Implementation: 00:07:00</p></div><div><div><p>•Multiple Linear Regression - How it works?: 00:01:00</p></div><div><div><p>•Multiple Linear Regression - Python Implementation: 00:09:00</p></div><div><div><p>•Decision Trees - How it works?: 00:05:00</p></div><div><div><p>•Random Forest - How it works?: 00:03:00</p></div><div><div><p>•Decision Trees and Random Forest - Python Implementation: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•kNN - How it works?: 00:02:00</p></div><div><div><p>•kNN - Python Implementation: 00:10:00</p></div><div><div><p>•Decision Tree Classifier and Random Forest Classifier in Python: 00:10:00</p></div><div><div><p>•SVM - How it works?: 00:04:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Apex Learning

Machine Learning with Python

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to clean, process, wrangle, and manipulate data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know how to create a resume for a data science job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get a grasp of NumPy by learning how to use it for numerical data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover the secret of different Python libraries, such as Pandas, Matplotlib, SciPy and many more</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore machine learning concepts and algorithms</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover developing custom data solutions</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">You can create various applications for science, math, machine learning, and data science with Python. As a high-level, general-purpose programming language, Python is quite English-like and easy to learn. This&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Python for Data Science and Machine Learning</b>&nbsp;course will show you how to use Python for machine learning and data science.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">During the course, you will master every useful Python skill you need for data science and machine learning. If you find it challenging to write complex Python programs, we&rsquo;ll show you how to do so with our step-by-step instructions based on actual work contexts. You&rsquo;ll also learn how to run the regression, classification, and clustering and the specifics of supervised and unsupervised machine learning. Moreover, this course covers how statistics work for data science, hypothesis testing, and much more.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Python for Data Science and Machine Learning&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To become a master of Python, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><br fr-original-style="" style="box-sizing: border-box;"></p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Python for Data Science &amp; Machine Learning from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science &amp; Machine Learning Concepts</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python For Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Statistics for Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Probability and Hypothesis Testing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">NumPy Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Pandas Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Data Visualization</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Loading &amp; Exploration</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Cleaning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Feature Selecting and Engineering</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear and Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K Nearest Neighbours</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Decision Trees</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Ensemble Learning and Random Forests</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Support Vector Machines</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K-Means</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">PCA</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science Career</strong></li></ul>

LearnDrive UK

This course aims to teach you how to use Python for machine learning and data science.



Python for Data Science and Machine Learning

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

Nexus Human

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

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

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

Machine Learning 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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