Free Machine Learning eBooks

Free #MachineLearning eBooks:  #abdsc #BigData #DataScience via @eelrekab

  • 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.
  • 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.

Here are three eBooks available for free.
MACHINE LEARNING
Edited by Abdelhamid Mellouk and Abdennacer Chebira
Machine Learning can be defined in various ways…

@KirkDBorne: Free #MachineLearning eBooks: #abdsc #BigData #DataScience via @eelrekab

MACHINE LEARNING

Edited by Abdelhamid Mellouk and Abdennacer Chebira

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behaviour.

Machine Learning addresses more specifically the ability to improve automatically through experience.

UNDERSTANDING MACHINE LEARNING

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.

NEURAL NETWORKS

by D. 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.

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@Yvan, you need to click “click here” at the end of page, which redirects you to the following link:

The link is at the end of the sentence right above the “Top DSC Resources”. It goes to Lee Bakers Chi2 site.

Free Machine Learning eBooks