Mastering Machine Learning with scikit-learn

Table of Contents

1. The Fundamentals of Machine Learning
2. Simple linear regression
3. Classification and Regression with K Nearest Neighbors
4. Feature Extraction and Preprocessing
5. From Simple Regression to Multiple Regression
6. From Linear Regression to Logistic Regression
7. Naive Bayes
8. Nonlinear Classification and Regression with Decision Trees
9. From Decision Trees to Random Forests, and other Ensemble Methods
10. The Perceptron
11. From the Perceptron to Support Vector Machines
12. From the Perceptron to Artificial Neural Networks
13. Clustering with K-Means
14. Dimensionality Reduction with Principal Component Analysis