Machine Learning Made Visual with Python
- 1st Edition - September 1, 2026
- Latest edition
- Author: Weisheng Jiang
- Language: English
Machine Learning Made Visual with Python makes machine learning intuitive through Python coding and dynamic visualizations. It helps readers grasp complex math concepts by showin… Read more
Machine Learning Made Visual with Python makes machine learning intuitive through Python coding and dynamic visualizations. It helps readers grasp complex math concepts by showing how algorithms evolve step by step. The book helps readers 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 – each machine learning concept is explained through rich, interactive visualizations, helping readers build geometric and conceptual understanding without relying solely on formulas
- Every chapter includes well-documented Python code to implement algorithms from scratch, encouraging hands-on practice and deeper comprehension
- The book includes step-by-step mathematical breakdowns – core mathematical tools (e.g., linear algebra, probability, optimization) 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
- Practical examples and coding exercises help readers bridge the gap between academic theory and real-world application in data science and quantitative industries
Senior students and researchers in data science, machine learning, artificial intelligence, and quantitative finance. Readers typically include senior undergraduates, graduate students and lecturers in computer science, engineering, statistics, and applied mathematics
1. Introduction to Machine Learning
2. 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
2. 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
- Edition: 1
- Latest edition
- Published: September 1, 2026
- Language: English
WJ
Weisheng Jiang
Dr Jiang holds a PhD in engineering; he is currently Vice President of Solactive, a global fintech firm, where he leads initiatives that integrate machine learning into financial index and data solutions. Before this, he worked at MSCI for seven years, where he was involved in quantitative research, systematic investing, and the application of machine learning in real-world financial systems
Affiliations and expertise
Vice President, Solactive, China