Machine Learning with Python for Everyone brings together all they’ll need to succeed: a practical understanding of the machine learning process, accessible code, skills for implementing that process with Python and the scikit-learn library, and real expertise in using learning systems intelligently.

The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing.

Rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this short book offers a practical and high-level introduction to the practical components and statistical concepts found in machine learning.

The book Machine Learning: Hands-On for Developers and Technical Professionals contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along.

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.

This Practical Book Shows You How. By Using Concrete Examples, Minimal Theory, And Two Production-Ready Python Frameworks―Scikit-Learn And Tensorflow―Author Aurélien Géron Helps You Gain An Intuitive Understanding Of The Concepts And Tools For Building Intelligent Systems

This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python.

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.

This book explores the concepts and techniques of knowledge discovery and data mining. As amultidisciplinaryfield, datamining draws on work from areas including statistics, machine learning, pattern recognition, database technology, information retrieval, network science, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization.