An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.

The book's contents is a large inventory of numerous topics relevant to DL job interviews and graduate level exams. That places this work at the forefront of th

Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production.

Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonpa

Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and