The subject of this book is automated learning, or, as we will more often call it, Machine Learning (ML). That is, we wish to program computers so that they can “learn” from input available to them. Roughly speaking, learning is the process of converting experience into expertise or knowledge. The input to a learning algorithm is training data, representing experience, and the output is some expertise, which usually takes the form of another computer program that can perform some task. Seeking a formal-mathematical understanding of this concept, we’ll have to be more explicit about what we mean by each of the involved terms: What is the training data our programs will access? How can the process of learning be automated? How can we evaluate the success of such a process (namely, the quality of the output of a learning program)?
Understanding Machine Learning from theory to algorithms – Shai Shalev-Shwartz & Shai Ben-David
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
An introduction to neural networks - Kevin Gurney & University of Sheffield
Intelligent Projects Using Python - Santanu Pattanayak
Python Machine Learning Eqution Reference - Sebastian Raschka
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Python Deep Learning Cookbook - Indra den Bakker
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Coding Theory - Algorithms, Architectures and Application
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Pattern recognition and machine learning - Christopher M.Bishop
Introduction to Deep Learning - Eugene Charniak
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Deep Learning and Neural Networks - Jeff Heaton
Artificial Intelligence by example - Denis Rothman
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Machine Learning with spark and python - Michael Bowles
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Java Deep Learning Essentials - Yusuke Sugomori
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf