Must-read Machine learning and data science books for post-graduate courses

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Regarding post-graduate coursework offered in IITs, I have compiled a list of books that are usually referred to.

Advanced Algorithms

  1. Foundations of Algorithms, Jones and Bartlett. [Site]
  2. Algorithm Design by Jon Kleinberg and Eva Tardos [PDF]

Social Computing :

  1. Networks, Crowds, and Markets – Easley and Kleinberg [PDF]
  2. Mining of Massive Datasets – Jure Leskovec, Anand Rajaraman, Jeff Ullman [PDF] – I bought this book and found it very useful. It introduces each topic and also explores the modification of the algorithm on a large scale, i.e. when running on a large number of data points.

Machine Learning

  1. Pattern Classification, Duda Hart – A fair warning that it contains a fair bit of maths and is recommended for those new to the field. [PDF]
  2. Tom Mitchell. Machine Learning (McGraw Hill) – I recommend this book if you are just starting. The concepts are explained quite lucidly. [PDF]
  3. Pattern Recognition – an Algorithmic Approach – I found this book quite easy to grasp.
  4. Machine Learning – a probabilistic perspective by Kevin P. Murphy – This book gives a good mathematical treatment in explaining Machine Learning concepts. [PDF]

Deep Learning

  1. Deep Learning by Ian Goodfellow and Yoshua Bengio, and Aaron Courville[PDF]

Natural Language Processing

  1. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition by Jurafsky and Martin [PDF]

Information Retrieval

  1. Introduction to Information Retrieval by Manning, Raghavan, and Schutze[PDF]

Final thoughts on machine learning and data science books

We thus learned about the most popular books that will allow you to get started in the exciting domain of machine learning and artificial intelligence. Welcome and all the very best for your journey ahead!

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