Machine Learning Made Visual with Python
- 1 Edición - 1 de septiembre de 2026
- Última edición
- Autor: Weisheng Jiang
- Idioma: Inglés
Machine Learning Made Visual with Python makes machine learning intuitive through Python coding and dynamic visualizations. The book helps readers grasp complex math concep… Leer más
Descripción
Descripción
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.
Puntos claves
Puntos claves
- 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
De interès para
De interès para
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
Índice
Índice
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
Detalles del producto
Detalles del producto
- Edición: 1
- Última edición
- Publicado: 1 de septiembre de 2026
- Idioma: Inglés
Sobre el autor
Sobre el autor
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
Afiliaciones y experiencia
Vice President, Solactive, China