New Book: Intuitive Machine Learning
Intuitive machine learning focused on explainable AI, human intelligence, visualizations and powerful applications. By Vincent Granville Ph.D, published September 2022. PDF format, 156 pages. Version 1.0 with Python code. The book is available here.
This book covers the foundations of machine learning, with modern approaches to solving complex problems. The focus is on scalability, automation, testing, optimization and interpretability (explainable AI). For example, I present regression techniques – including Logistic and Lasso – as a single method, without using advanced linear algebra. There is no need to learn 50 versions when doing everything and more. Confidence regions and prediction intervals are constructed using a parametric bootstrap, without statistical models or probability distributions. Models (including generative models and mixtures) are primarily used to create rich synthetic data to test and compare various methods.
Topics include clustering and classification, GPU machine learning, ensemble methods including original boosting technique, elements of graph modeling, deep neural networks, auto-regressive and non-periodic time series , Brownian motions and associated processes, simulations, interpolation, random numbers. , natural language processing (intelligent exploration, taxonomy creation and structuring of unstructured data), computer vision (pattern generation and recognition), curve fitting, cross-validation, fit metrics, feature selection, curve fitting , gradient methods, optimization techniques and numerical stability.
The methods come with enterprise-grade Python code, reproducible datasets and visualizations, including data animations (gifs, videos, even audio made in Python). The code uses various data structures and library functions sometimes with advanced options. It is a Python tutorial in itself, and an introduction to scientific computing. Some data animations and chart enhancements are done in R. Code, datasets, spreadsheets, and data visualizations are also on GitHub.
The chapters are mostly independent of each other, allowing you to read in random order. A glossary, an index and numerous cross-references facilitate navigation and unify all the chapters. The style is very compact, gets straight to the point quickly, and is suitable for professionals who want to learn a lot of useful material in a limited amount of time. Jargon and obscure theories are absent, replaced by plain English to make it easier for non-experts to read, and to help you discover topics usually inaccessible to beginners.
Although the state of the art of research is presented in all chapters, the prerequisites for reading this book are minimal: analytical professional training, or a first course in differential calculus and linear algebra. The original presentation avoids all unnecessary math and statistics, without eliminating advanced topics.
For the detailed table of contents and related material (Python code, etc.), visit the GitHub page about this book, here. To get your copy, follow this link.
About the Author
Vincent Granville is a pioneering data scientist and machine learning expert, co-founder of Data Science Central (acquired by TechTarget in 2020), former VC-funded executive, author, and patent owner. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, CNET, InfoSpace. Vincent is also a former post-doctoral fellow at the University of Cambridge and the National Institute of Statistical Sciences (NISS).
Vincent published in Journal of Number Theory, Journal of the Royal Statistical Society (Series B), and IEEE Transactions on Pattern Analysis and Artificial Intelligence. He is also the author of several books. He lives in Washington State and enjoys researching stochastic processes, dynamical systems, experimental mathematics, and probabilistic number theory.