There are a lot of textbooks available online. Most modern textbooks focus on neural networks or deep learning. While these are important topics, our goal in this course is to give you a broad perspective of the field. Technology changes quickly, so you need to have a good background in the fundamentals, and need to understand how to do more than just "button push."

Those wishing to build their theoretical background in this area will find this book useful:

      M.P. Deisenroth, A.A. Faisal and C.S. Ong
      Mathematics for Machine Learning
      Cambridge University Press, ISBN: 978-1-108-47004-9, 2020.
      URL: https://mml-book.com/

One of the best classical textbooks on this topic is:

      R.O. Duda, P.E. Hart, and D.G. Stork
      Pattern Classification
      Wiley Interscience, ISBN: 0-13-022616-5, 2001.
      URL: https://isip.piconepress.com/courses/temple/ece_8527/resources/dhs_book/

Other excellent theoretical treatments are:

      C.M. Bishop
      Pattern Recognition and Machine Learning
      Springer, ISBN: 978-0387310732, 2003.

      D.J.C. MacKay
      Information Theory, Inference and Learning Algorithms
      Cambridge University Press, ISBN: 978-0521642989, 2004.

Good online resources that focus more on neural networks and implementation details are:

      Michael Nielson
      Neural Networks and Deep Learning
      URL: http://neuralnetworksanddeeplearning.com

and

      Jake VanderPlas
      Python Data Science Handbook
      URL: https://github.com/jakevdp/PythonDataScienceHandbook