4 ms·

Would you suggest any books/resources to learn the theory behind these implementations so a newbie can follow along?

• jumpCastle 10y ago
  Introduction to statistical learning http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/
  • eggie5 10y ago
    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.
  • IshV 10y ago
    This book as a prerequisite to anyone who wants to get into machine learning imo.
• make3 10y ago
  probably this https://www.amazon.ca/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130 https://www.amazon.ca/Python-Machine-Learning-Sebastian-Rasc...
  • nkozyra 10y ago
    While a great book, most of this is just implementations of sklearn.
• asteinbr 10y ago
  I can recommend Applied Predictive Modeling by Max Kuhn: http://appliedpredictivemodeling.com/ http://appliedpredictivemodeling.com/
• saboot 10y ago
  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
  • tnecniv 10y ago
    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.
  • victor106 10y ago
    Thanks everyone for the suggestions...will check these books out
  • capkutay 10y ago
    How does that compare to Andrew Ng's course?