Pattern recognition and machine learning by Bishop is one of the canonical text books. It helps to have a linear algebra background, it includes a refresher though

Bishop is good but reads a little too much like a literature review sometimes. That may or may not be a problem depending on what you are looking for.

Thanks everyone for the suggestions...will check these books out

How does that compare to Andrew Ng's course?

I can recommend Applied Predictive Modeling by Max Kuhn: http://appliedpredictivemodeling.com/

Introduction to statistical learning http://www-bcf.usc.edu/~gareth/ISL/

This one is interesting to see the "statistical" other side of the industry vs machine-learning people. For example I don't think gradient descent is used once in that book.

This book as a prerequisite to anyone who wants to get into machine learning imo.

probably this https://www.amazon.ca/Python-Machine-Learning-Sebastian-Rasc...

While a great book, most of this is just implementations of sklearn.