# 35 Free Online Books on Machine Learning

# 35 Free Online Books on Machine Learning

### Look at a comprehensive list of 35 free books on machine learning (and related fields) that are freely available online (in pdf format) for self-paced learning.

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**35 free books**on

**machine learning**(& related fields) which are

**freely available online (in pdf format)**for

**self-paced learning**. Please feel free to comment/suggest if I missed to mention one or more important books that you like and would like to share. Also, sorry for the typos.

Following are the key areas under which the books are categorized:

- Pattern Recognition & Machine Learning
- Probability & Statistics
- Neural Networks & Deep Learning

## List of 35 Free eBooks on Machine Learning and Related Fields

The following is a list of **35 free online ebooks (pdf format)** that can be used for learning ML at your own pace.

**Pattern Recognition and Machine Learning**- Foundations of Machine Learning
- The Elements of Statistical Learning – Trever Hastie, Robert Tibshirani, Jerome Friedman
- Machine Learning: A Probabilistic Approach: Authored by Kevin P. Murphy, the summary details of this book could be found on following page.
- Pattern Recognition & Machine Learning – Christopher M. Bishop: This book is a great book but if you are not the one who loves Maths, it may go out and scare you enough. So, get your mathematics fundamentals good enough and get started with it.
- Information Theory, Inference, and Learning Algorithms (David Mackay)
- Pattern Recognition: Authored by Sergios Theodoridis, Konstantinos Koutroumbas
- A Probabilistic Theory of Pattern Recognition. Devroye, Gyorfi, Lugosi.
- Introduction to Machine Learning. Smola and Vishwanathan
- Machine Learning and Bayesian Reasoning. David Barber
- Gaussian Processes for Machine Learning. Rasmussen and Williams
- Introduction to Information Retrieval. Manning, Rhagavan, Shutze
- Forecasting: principles and practice. Hyndman, Athanasopoulos. (Online Book)
- Introduction to Machine Learning; Shashua
- Reinforcement Learning; Weber et al.
- Machine Learning; Mellouk & Chebira
- Bayesian Reasoning and Machine Learning
- Probabilistic Programming and Bayesian Methods for Hackers
- A Course in Machine Learning
- Data Mining: Practical Machine Learning Tools and Techniques
- Machine Learning Evaluation: A Classification Perspective
- Introduction to Machine Learning in Python with scikit-learn
- The LION Way: Machine Learning plus Intelligent Optimization – Roberto Battiti, Mauro Brunato
- A First Encounter with Machine Learning – Max Welling
- Practical Artificial Intelligence Programming in Java – Mark Watson
- Machine Learning – The Art & Science of Algorithms that Make Sense of Data – Peter Flach

**Probability and Statistics**- All of Statistics: Authored by L. Wasserman, the details of this book could be further found on this page.
- Introduction to statistical thought. Lavine
- Basic Probability Theory. Robert Ash
- Introduction to probability. Grinstead and Snell
- Stanford Statistics Learning Class – Lecture Notes

**Neural Networks and Deep Learning**- Draft Textbook on Deep Learning: This is a draft textbook from Yoshua Bengio, Ian Goodfellow and Aaron Courville is the most comprehensive treatment of deep learning.
- Neural Networks and Deep Learning: Free draft e-book entitled “Neural Networks and Deep Learning” authored by Michael Nielsen whose work could be found on his personal website, MichaelNielson.org.
- Unsupervised Feature Learning and Deep Learning
- Machine Learning, Neural and Statistical Classiﬁcation; Michie & Spiegelhalter
- Machine Learning, Neural and Statistical Classification – D. Michie, D. J. Spiegelhalter

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