Every month we scour the internet seeking out free eBooks to help you on your educational journey, and this month has been no different.
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This month, we have 3 books about Machine Learning. They're all FREE, so get cracking...
- Data Mining: Practical Machine Learning Tools and Techniques
- Understanding Machine Learning: From Theory to Understanding
- A Brief Introduction to Neural Networks
by Ian H. Witten, Eibe Frank and Mark A. Hall
Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This book on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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.
Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects.
Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods.
by Shai Ben-David and Shai Shalev-Shwartz
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
by David Kriesel
The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning.
After working through the book you will have written code that uses neural networks and deep learning to solve complex pattern recognition problems.
And you will have a foundation to use neural networks and deep learning to attack problems of your own devising.