Here is the full list of best reference books on Machine Learning Algorithms.
|1. “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World” by Pedro Domingos|
|2. “Fundamentals of Machine Learning for Predictive Data Analytics – Algorithms, Worked Examples, and Case Studies” by John D. Kelleher and Brian Mac Namee|
|3. “Understanding Machine Learning: From Theory To Algorithms” by Shai Shalev-Shwartz|
|4. “Pattern Recognition and Machine Learning (Information Science and Statistics)” by Christopher M. Bishop|
|5. “Machine Learning in Python” by Michael Bowles|
|6. “Practical Machine Learning: Innovations in Recommendation” by Ted Dunning and Ellen Friedman|
|7. “Machine Learning For Beginners Guide Algorithms: Supervised & Unsupervsied Learning. Decision Tree & Random Forest Introduction” by William Sullivan|
|8. “Machine Learning Algorithms” by Giuseppe Bonaccorso|
|9. “Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms” by Nikhil Buduma and Nicholas Locascio|
|10. “Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning With Real-World Applications” by Joshua Chapmann|
If any more book needs to be added to the list of best books on Machine Learning Algorithms Subject, please let us know.
Sanfoundry Global Education & Learning Series – Best Reference Books!