EGEA Spa, 2020. — 204 p. — ISBN: 978-88-99902-65-0. This book gives the fundamental principles for developing Machine Learning applications with Python. Introduction to Machine Learning Linear Models for Machine Learning Beyond Linearity: Ensemble Methods for ML An Introduction to Modern ML Techniques A crash course in Python Mathematics behind the skip-gram model
Independently published, 2019. — 369 p. — ISBN: 978-1686500237, 1686500238. Your Guide to Getting Ahead with Python! Today, several commercial apps and research projects make use of machine learning, but this field is not only meant for big companies with extensive research teams, a beginner can get started, too. Machine Learning came into prominence in the 1990s, when...
Springer, 2019. — 263 p. — ISBN: 978-3030157289, 3030157288. Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts...
Packt Publishing, 2016. — 614 p. — ISBN10: 178439968X. — ISBN13: 978-1784399689. This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. About This Book Fully-coded working examples using a wide...
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