I am learning from Pattern Recognition and Machine Learning, Chris Bishop any good resources? Are there any videos or other books/notes that anyone has come across that follow Pattern Recognition and Machine Learning by Chris Bishop? I bought this book to learn Machine Learning and am having some trouble getting through it. 
 A: I would recommend these resources to you:


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*Tom Mitchell: Carnegie Mellon University

*(Only for Supervised Learning and follows Bishop) Pattern Recognition: Indian Institute of Science (I personally like this course as I have attended it, but this course requires you to know probability theory.)


Both the courses are maths oriented, for a lighter course on machine learning would be "Machine Learning" by Udacity
A: https://www.cs.toronto.edu/~rsalakhu/STA4273_2015/
This course closely follows part of Bishop's. It has lecture videos with it.
A: jupyter notebooks with python implementations and scikit-learn usage at PRML
A: I think an often overlooked book is Information Theory, Inference, and Learning Algorithms by David MacKay.
It follows the general framework of PRML, since the authors seem to have a similar (at least in my view) perspective. Depending on your background -- whether or not you enjoy concepts like information theory/coding/KL-divergence -- you may find this book extremely eye-opening. 
A: Bishop is a great book. I hope these suggestions help with your study:


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*The author himself has posted some slides for Chapters 1, 2, 3 & 8, as well as many solutions.

*A reading group at INRIA have posted their own slides covering every chapter.

*João Pedro Neto has posted some notes and workings in R here. (Scroll down to where it says "Bishop's Pattern Recognition and ML")

*Many introductory machine learning courses use Bishop as their textbook. Googling gives a few different ones; have a look and see which topics and focus you prefer.

