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• Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten
• Reinforcement Learning: An Introduction by Richard S. Sutton
• Artificial Intelligence: A Modern Approach by Stuart J. Russell
• The Hundred-Page Machine Learning Book by Andriy Burkov
• Introduction to Deep Learning (The MIT Press) by Eugene Charniak
• Neural Networks and Deep Learning: A Textbook by Charu C. Aggarwal
• Deep Learning with Python by François Chollet
• Deep Learning: A Practitioner's Approach by Josh Patterson
• Python Machine Learning by Sebastian Raschka
• Machine Learning with Spark by Nick Pentreath
• Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman
• Coding the Matrix: Linear Algebra through Applications to Computer Science by Philip N. Klein
• Machine Learning: The Art and Science of Algorithms that Make Sense of Data by Peter Flach
• Bandit Algorithms for Website Optimization by John Myles White
• Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
• Mahout in Action by Sean Owen
• Probabilistic Graphical Models: Principles and Techniques by Daphne Koller
• The Essence of Neural Networks (Essence of Computing) by Robert Callan
• Programming Collective Intelligence: Building Smart Web 2.0 Applications by Toby Segaran
• Pattern Recognition and Machine Learning by Christopher M. Bishop

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