12 Best Free Ebooks for Machine Learning

Machine learning is a scientific discipline that works on construction and study of algorithms which operate by building a model from example inputs using the make predictions or decisions. With the number of people joining the nerdy geeks, machine learning has seen quite a lot of development over the course of years.

Are you the one who has recently started or is planning to start the career in machine learning? If your answer is yes, I won’t scare you with the words like it’s quite a difficult job but then its hard nut to crack and if you take it as a motivation you will understand what I am trying to say. Many things in this world are difficult or they appear to be in the beginning but are not impossible.

Machine learning is not everyone’s cup of tea but then if you are determined to contribute then we are here to help you though not directly like writing a book/blog or something like that but then with the list of useful resources. Here, we have compiled a list of best free machine learning ebooks that are written with the aim to help you targeted nerdy people in machine learning as easily as possible. So, take a look at the best free machine learning ebooks listed below and make your pick as to which one you would want to read first and then go along with other ones.

1. Bayesian Reasoning and Machine Learning by David Barber

Bayesian Reasoning and Machine Learning by David Barber is an ebook that is designed for final-year undergraduates and master’s students with limited background in linear algebra and calculus. From basic reasoning to advanced techniques within the framework of graphical models, readers get to learn the developing skills as easily as they could wish.


 2. Inductive Logic Programming: Techniques and Applications
by Nada Lavrac, Saso Dzeroski

This book on inductive logic programming (ILP) that a research field at the intersection of machine learning and logic programming aims at a formal framework besides providing the practical algorithms for inductively learning relational descriptions in the form of logic programs.


3. Gaussian Processes for Machine Learning by Carl E. Rasmussen, Christopher K. I. Williams

This book makes readers learn a principled, practical, probabilistic approach to learning in kernel machines in quite a different and easy way. It comprises of supervised-learning problem for regression and classification, and includes detailed algorithms.


4. Machine Learning, Neural and Statistical Classification by D. Michie, D. J. Spiegelhalter

This book by D.Michie, D. J. Spiegelhalter is written with the aim to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets and also draw conclusions on their applicability to realistic industrial problems.


5. Information Theory, Inference, and Learning Algorithms by David J. C. MacKay

In this book Information theory topic is explained and well talked about in detail to help readers attain good knowledge about the practical communication systems like arithmetic coding for data compression and sparse-graph codes for error-correction.


6. The Elements of Statistical Learning: Data Mining, Inference, and Prediction BY by T. Hastie, R. Tibshirani, J. Friedman

With this book, readers get to learn conceptual underpinnings rather than just obtaining theorical knowledge. It comprises of statistical framework and is the best pick for statisticians, researchers and practitioners.


7. The LION Way by Roberto Battiti, Mauro Brunato

The LION Way is an ebook that is written with the aim to help readers in machine learning and Intelligent Optimization (LION) that is the combination of learning from data and optimization applied to solve complex and dynamic problems.


8. Introduction to Machine Learning by Amnon Shashua

Introduction to Machine learning is an ebook that comprises of Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering) and PAC learning (the Formal model, VC dimension, Double Sampling theorem).


9. A Course in Machine Learning by Hal Daume III

A Course in Machine Learning is an ebook that comprises of a set of introductory material covering various aspects of modern machine learning.


10. Reinforcement Learning by C. Weber, M. Elshaw, N. M. Mayer

The book comprises of know how of reinforcement learning. There are 22 chapters in all. While first 11 chapters focus on description and extended scope of reinforcement learning, the remaining 11 chapters show that there is already wide usage in numerous fields.


11. Introduction To Machine Learning by Nils J Nilsson

As the name says, this is an introduction to machine learning. The book by Nils J Nilsson surveys topics in machine learning circa 1996 with the aim to pursue a middle ground between theory and practice.


12. Reinforcement Learning: An Introduction by Richard S. Sutton, Andrew G. Barto

Reinforcement learning lets users learn machine in an easy way. Its like an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment.


If you have already read any one or few ebooks listed above, which one would you suggest others to go for in the first go? Do share your reviews once you have gone through these free machine learning ebooks.


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