Books

Books are not required for the course. All of the books are suggested.

  1. Machine Learning with R, the tidyverse, and mlr by Hefin I. Rhys. The book is available on Manning Publications website. The hard copy of the book is not required. Online copy is fine.

  1. Python Data Science Handbook: Essential Tools for Working with Data by Jake VanderPlas. The book is available on Github. The hard copy of the book is not required. Online copy is fine. This is the first edition of the book. The second edition of the book is also available. If you are interested, you can buy one from Amazon.

  1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron. The book is available on Amazon. The hard copy of the book is not required. A pdf copy of the book is available from me on request.

  1. R for Data Science by Hadley Wickham and Garrett Grolemund is an excellent book to learn about the basics of R. The online version of 2nd Edition of the book is available free.

  1. To learn about the mathematics underlying many Machine Learning (ML) algorithms, Mathematics for Machine Learning by Marc Deisenroth, A Aldo Faisal, and Cheng Ong can be used -

  1. To learn about the Ethics in Machine Learning, Fairness and Machine Learning by Solon Barocas, Mortiz Hardt, and Arvind Narayanan can be used. The book is not needed to be purchased. An online version of the book is available.

  1. To learn about how to deploy Machine Learning Models into Production, Machine Learning in Production: From Models to Products by Christian Kästner can be used. The book is not needed to purchase. An online version of the book is available. A github repository for the course is also available here and here.

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