Beginner Machine Learning in Python + ChatGPT Bonus [2023]

Build a solid foundation in Machine Learning: Linear Regression, Logistic Regression and K-Means Clustering in Python

Created by Hadelin de Ponteves, Kirill Eremenko | 3.5 hours on-demand video course

This course has 3 main sections: First, we will dive into Regression, where we will learn to predict continuous variables and we will cover foundational concepts like Simple and Multiple Linear Regression, Ordinary Least Squares, Testing your Model, R-Squared and Adjusted R-Squared.

In the second section you will master Logistic Regression, which is by far the most popular model for Classification. We will learn all about Maximum Likelihood, Feature Scaling, The Confusion Matrix, Accuracy Ratios…. and you will build your very first Logistic Regression!

The third and final section is all about Clustering. We will investigate the concepts of unsupervised learning and you will practice using K-Means Clustering to discover previously unseen patterns in your data.

What you’ll learn

• Machine Learning
• The Machine Learning Process
• Regression
• Ordinary Least Squares
• Simple Linear Regression
• Splitting your data into a Training set and a Test set
• Multiple Linear Regression
• R-Squared
• Adjusted R-Squared
• Classification
• Maximum Likelihood
• Feature Scaling
• Confusion Matrix
• Accuracy
• Clustering
• K-Means Clustering
• The Elbow Method
• K-Means++
• Build Machine Learning models in Python
• Make Predictions

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