Top 30 Machine Learning Books You Should Read

 

    1. Pattern Recognition and Machine Learning by Christopher M. Bishop (2006) (amazon.com)‘Pattern
    2. The Elements of Statistical Learning: Data Mining, Inference, and
      Prediction
      by Trevor Hastie (2001) (amazon.com)‘The
    3. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy (2012) (amazon.com)‘Machine
    4. Deep Learning by Ian Goodfellow (amazon.com)‘Deep
    5. Machine Learning by Tom M. Mitchell (1986) (amazon.com)‘Machine
    6. An Introduction to Statistical Learning: With Applications in R by Gareth James (2013) (amazon.com)‘An
    7. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will
      Remake Our World
      by Pedro Domingos (2015) (amazon.com)‘The
    8. Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron (2017) (amazon.com)‘Hands-On
    9. Information Theory, Inference and Learning Algorithms by David J.C. MacKay (2002) (amazon.com)‘Information
    10. Python Machine Learning by Sebastian Raschka (2015) (amazon.com)‘Python
    11. Bayesian Reasoning and Machine Learning by David Barber (2012) (amazon.com)‘Bayesian
    12. Programming Collective Intelligence: Building Smart Web 2.0 Applications
      by Toby Segaran (2002) (amazon.com)‘Programming
    13. Learning From Data: A Short Course by Yaser S. Abu-Mostafa (2012) (amazon.com)‘Learning
    14. Artificial Intelligence: A Modern Approach by Peter Norvig (1994) (amazon.com)‘Artificial
    15. Probabilistic Graphical Models: Principles and Techniques by Daphne Koller (2009) (amazon.com)‘Probabilistic
    16. Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten (1999) (amazon.com)‘Data
    17. Machine Learning for Hackers by Drew Conway (2012) (amazon.com)‘Machine
    18. Machine Learning in Action by Peter Harrington (2011) (amazon.com)‘Machine
    19. Mining of Massive Datasets by Anand Rajaraman (2011) (amazon.com)‘Mining
    20. Neural Networks and Deep Learning by Michael Nielsen (2013) (amazon.com)‘Neural
    21. Reinforcement Learning: An Introduction by Richard S. Sutton (1998) (amazon.com)‘Reinforcement
    22. Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz (2014) (amazon.com)‘Understanding
    23. Building Machine Learning Systems with Python by Willi Richert (2013) (amazon.com)‘Building
    24. Pattern Classification by David G. Stork (1973) (amazon.com)‘Pattern
    25. Gaussian Processes for Machine Learning by Carl Edward Rasmussen (2005) (amazon.com)‘Gaussian
    26. Introduction to Machine Learning by Ethem Alpaydin (2004) (amazon.com)‘Introduction
    27. Data Science from Scratch: First Principles with Python by Joel Grus (2015) (amazon.com)‘Data
    28. Applied Predictive Modeling by Max Kuhn (2013) (amazon.com)‘Applied
    29. Make Your Own Neural Network by Tariq Rashid (amazon.com)‘Make
    30. Bayesian Data Analysis by Andrew Gelman (1995) (amazon.com)‘Bayesian
    31. Deep learning with Python by Francois Chollet (amazon.com)‘Deep
    32. Neural Networks for Pattern Recognition by Christopher M. Bishop (1996) (amazon.com)‘Neural
    33. Python for Data Analysis by Wes McKinney (2011) (amazon.com)‘Python
    34. Convex Optimization by Stephen Boyd (2004) (amazon.com)‘Convex
    35. Deep Learning: A Practitioner’s Approach by Josh Patterson (2015) (amazon.com)‘Deep
    36. An Introduction to Support Vector Machines and Other Kernel-Based Learning
      Methods
      by Nello Cristianini (2000) (amazon.com)‘An
    37. Introduction to Machine Learning with Python: A Guide for Data Scientists
      by Andreas C. Müller (2015) (amazon.com)‘Introduction
    38. All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman (2003) (amazon.com)‘All
    39. Machine Learning with R by Brett Lantz (2013) (amazon.com)‘Machine
    40. Data Smart: Using Data Science to Transform Information into Insight
      by John W. Foreman (2013) (amazon.com)‘Data

 

  1. Speech and Language Processing: An Introduction to Natural Language
    Processing, Computational Linguistics and Speech Recognition
    by Dan Jurafsky (2000) (amazon.com)‘Speech
  2. Machine Learning: An Algorithmic Perspective by Stephen Marsland (2009) (amazon.com)‘Machine
  3. Python Data Science Handbook: Tools and Techniques for Developers
    by Jake Vanderplas (2016) (amazon.com)‘Python
  4. Real-World Machine Learning by Henrik Brink and Joseph W. Richards (2016) (amazon.com)‘Real-World
  5. Machine Learning: The Art and Science of Algorithms That Make Sense of Data
    by Peter Flach (2012) (amazon.com)‘Machine
  6. Fundamentals of Deep Learning: Designing Next-Generation Artificial
    Intelligence Algorithms
    by Nikhil Buduma (2015) (amazon.com)‘Fundamentals

 

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