Machine Learning Made Visual with Python

Weisheng Jiang

• Publisher: Elsevier
• Year: 2026
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• eBook ISBN:
  9780443516726
• Index: Computer Science

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. - Includes visual intuition of algorithms, with each machine learning concept explained through rich, interactive visualizations - Provides well-documented Python code to help readers implement algorithms from scratch, thus encouraging hands-on practice and deeper comprehension - Presents step-by-step mathematical breakdowns – core mathematical tools (e.g., linear algebra, probability, optimization) that are demystified and connected directly to algorithm behavior - Covers a wide range of algorithms, from linear regression to kernel PCA and EM clustering, making it suitable for both beginners and experienced learners seeking clarity

Table of contents

• Title of Book
• Chapter 1. Machine Learning: From Concepts to Practice
• Chapter 2. Mathematical Backbone: Vectors as Building Blocks of Machine Learning
• Chapter 3. The Magical World of Matrices: Building Blocks for Machine Learning
• Chapter 4. Simple Linear Regression: From a Line to a Model
• Chapter 5. Multivariate Linear Regression
• Chapter 6. Polynomial Regression: Making Linear Models Go Nonlinear
• Chapter 7. Probability and Statistics: Foundations for Machine Learning
• Chapter 8. Logistic Regression: From Sigmoid Curves to Classification Boundaries
• Chapter 9. Regularized Regression – Taming Complexity in Machine Learning
• Chapter 10. Eigenvalue Decomposition: Seeing Transformations From Within
• Chapter 11. Covariance Matrix: Understanding Feature Relationships
• Chapter 12. Gaussian Distributions: The Bell Curve in Machine Learning
• Chapter 13. Bayesian Linear Regression – Learning From Both Data and Belief
• Chapter 14. Gaussian Processes: Modeling Uncertainty in Functions
• Chapter 15. k-Nearest Neighbor Classification: Learning by Proximity
• Chapter 16. Distance Measures in Data – Understanding Similarity and Difference
• Chapter 17. Naive Bayes Classification: Predicting With Probabilities
• Chapter 18. Gaussian Discriminant Analysis: From Circles to Ellipses
• Chapter 19. Support Vector Machine: Finding the Perfect Margin
• Chapter 20. The Kernel Trick: Turning Lines Into Curves
• Chapter 21. Decision Trees: From Simple Splits to Smart Predictions
• Chapter 22. Principal Component Analysis: Explained Through Ellipse
• Chapter 23. Principal Component Analysis in Action: From Theory to Real Data
• Chapter 24. Singular Value Decomposition: Understanding the Geometry of Matrices
• Chapter 25. Truncated Singular Value Decomposition: From Geometry to Image Compression
• Chapter 26. Kernel Principal Component Analysis: Unlocking Nonlinear Dimensions
• Chapter 27. Linear Discriminant Analysis: Pushing Classes Apart
• Chapter 28. K-Means Clustering: Chasing the Centroids
• Chapter 29. Gaussian Mixture Models — When One Bell Curve Isn't Enough
• Chapter 30. Mean Shift: Climbing Toward Peaks of Density
• Chapter 31. Hierarchical Clustering: Building a Tree From the Data
• Chapter 32. Density-Based Clustering: From Density Peaks to Natural Clusters
• Chapter 33. Spectral Clustering: Finding Hidden Communities in Networks
• Index