Machine Learning Made Visual with Python

Erscheinungsjahr 2026
ISBN: 978-0-443-51671-9
Verlag: Elsevier - Health Sciences Division

Buch, Englisch, 566 Seiten, Format (B × H): 229 mm x 150 mm, Gewicht: 890 g

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.

Autoren/Hrsg.

Jiang, Weisheng

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen

Inhaltsverzeichnis

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

Über Autor(innen)

Jiang, Weisheng
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