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
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Java Deep Learning Essentials - Yusuke Sugomori
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Neural Networks - A visual introduction for beginners - Michael Taylor
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Learn Keras for Deep Neural Networks - Jojo Moolayil
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Pattern recognition and machine learning - Christopher M.Bishop
Deep Learning with Theano - Christopher Bourez
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Deep Learning with Applications Using Python - Navin Kumar Manaswi
The hundred-page Machine Learning Book - Andriy Burkov
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Introduction to Scientific Programming with Python - Joakim Sundnes
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Deep Learning and Neural Networks - Jeff Heaton
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Deep Learning with Python - Francois Chollet
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Fundamentals of Deep Learning - Nikhil Bubuma
Python Machine Learning - Sebastian Raschka
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
