The Machine Learning in Python Series: Level 1 (Beginners)

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

Created by Hadelin de Ponteves, Kirill Eremenko | 4 hours of video course

In this course you will master the foundations of Machine Learning and practice building ML models with real-world case studies. The Course Objectives are the following: Get the right basics of how machine learning works and how models are built. Understand what is regression. Understand the theory behind the linear regression model. Know how to build, train and evaluate a linear regression model for a real-world case study. Understand what is classification. Understand the theory behind the logistic regression model. Understand and apply feature scaling including both normalization and standardization. Know how to build, train and evaluate a logistic regression model for a real-world case study. Understand what is clustering. Understand the theory behind the k-means clustering model. Know how to build, train and evaluate the k-means clustering model for a real-world case study.

What you’ll learn

• Machine Learning
• The Machine Learning Process
• Regression
• Ordinary Least Squares
• Simple Linear Regression
• 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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