Machine Learning Made Visual with Python makes machine learning intuitive through Python coding and dynamic visualizations. The book helps readers grasp complex math concepts by showing how algorithms evolve step-by-step. Readers will learn how to develop a hands-on, visual, and practical path to mastering core machine learning algorithms. Importantly, the book includes practical examples and coding exercises.
Table of Contents
1. Introduction to Machine Learning2. Regression Analysis
3. Multivariate Linear Regression
4. Nonlinear Regression
5. Regularization
6. Bayesian Regression
7. Gaussian Processes
8. k-Nearest Neighbour Classification
9. Naive Bayes Classification
10. Gaussian Discriminant Analysis (GDA)
11. Support Vector Machines (SVM)
12. Kernel Methods
13. Decision Trees
14. Principal Component Analysis (PCA)
15. Truncated Singular Value Decomposition (SVD)
16. Advanced PCA Techniques
17. PCA and Regression
18. Kernel PCA
19. Canonical Correlation Analysis (CCA)
20. k-Means Clustering
21. Gaussian Mixture Models (GMM)
22. Expectation-Maximization (EM) Algorithm
23. Hierarchical Clustering
24. Density-Based Clustering (e.g., DBSCAN)
25. Spectral Clustering
