This article represents a comprehensive list of

**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 books are categorized:

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

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

Following is a list of **35 FREE online ebooks (pdf format)** which could be used for learning ML at your own pace.

**Pattern Recognition & 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 & 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 & 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

### Ajitesh Kumar

Ajitesh has been recently working in the area of AI and machine learning. Currently, his research area includes Safe & Quality AI. In addition, he is also passionate about various different technologies including programming languages such as Java/JEE, Javascript and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc.

He has also authored the book, Building Web Apps with Spring 5 and Angular.

He has also authored the book, Building Web Apps with Spring 5 and Angular.

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