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

About the Author

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

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