21 Best Machine Learning Books

Here are 21 best machine learning books of all time:

  1. “The Elements of Statistical Learning: Data Mining, Inference, and Prediction” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
  2. “Pattern Recognition and Machine Learning” by Christopher Bishop.
  3. “Machine Learning: A Probabilistic Perspective” by Kevin Murphy.
  4. “An Introduction to Machine Learning” by Alpaydin.
  5. “Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow” by Sebastian Raschka and Vahid Mirjalili.
  6. “Machine Learning Yearning” by Andrew Ng.
  7. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
  8. “Data Mining: Practical Machine Learning Tools and Techniques” by Ian H. Witten, Eibe Frank, and Mark A. Hall.
  9. “Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron.
  10. “Reinforcement Learning: An Introduction” by Richard S. Sutton and Andrew G. Barto.
  11. “Bayesian Reasoning and Machine Learning” by David Barber.
  12. “Understanding Machine Learning: From Theory to Algorithms” by Shai Shalev-Shwartz and Shai Ben-David.
  13. “Machine Learning: The Art and Science of Algorithms that Make Sense of Data” by Peter Flach.
  14. “Programming Collective Intelligence” by Toby Segaran.
  15. “Grokking Deep Learning” by Andrew Trask.
  16. “Introduction to Machine Learning with Python” by Andreas Müller and Sarah Guido.
  17. “The Hundred-Page Machine Learning Book” by Andriy Burkov.
  18. “Applied Predictive Modeling” by Max Kuhn and Kjell Johnson.
  19. “Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython” by Wes McKinney.
  20. “Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using TensorFlow 2” by Anirudh Koul, Siddha Ganju, and Meher Kasam.
  21. “Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking” by Foster Provost and Tom Fawcett.

These books cover various topics related to machine learning, including statistics, probability, deep learning, data mining, and more. They are great resources for both beginners and advanced learners and provide practical insights and techniques for applying machine learning to real-world problems.