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Machine Learning Made Visual with Python de Weisheng Jiang

Machine Learning Made Visual with Python

Autor Weisheng Jiang
en Limba Engleză Paperback – sep 2026
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
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Specificații

ISBN-13: 9780443516719
ISBN-10: 0443516715
Pagini: 566
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE

Cuprins

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