Let’s clear up one potential source of confusion at the outset. What’s the difference between Machine Learning and Deep Learning? The two terms mean different things.
In essence, Machine Learning is the practice of using algorithms to parse data, learn insights from that data, and then make a determination or prediction. The machine is ‘trained’ using huge amounts of data.
Deep Learning is a subset of Machine Learning that uses multi-layers artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation and others. Think of Machine Learning as cutting-edge, and Deep Learning as the cutting-edge of the cutting-edge.
Both Machine Learning and Deep Learning are changing the world. Deep Learning is trending.
This roundup picks some useful books to learn about machine learning. The books are free to read. The article is divided into 3 pages.
1. Understanding Machine Learning by Shai Shalev-Shwartz and Shai Ben-David
The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and nonexpert readers in statistics, computer science, mathematics, and engineering.
2. Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy
This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory.
The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.
3. Probabilistic Machine Learning: Advanced Topics by Kevin Patrick Murphy
An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality.
This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.
4. Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, the authors aim to provide the necessary mathematical skills to read those other books.
Part I: Mathematical Foundations:
- Introduction and Motivation
- Linear Algebra
- Analytic Geometry
- Matrix Decompositions
- Vector Calculus
- Probability and Distribution
- Continuous Optimization
Part II: Central Machine Learning Problems
- When Models Meet Data
- Linear Regression
- Dimensionality Reduction with Principal Component Analysis
- Density Estimation with Gaussian Mixture Models
- Classification with Support Vector Machines
5. An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
This book provides an introduction to statistical learning methods.
It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences.
The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.
6. An Introduction to Statistical Learning with Applications in Python by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani and Jonathan Taylor
This book provides an introduction to statistical learning methods.
It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences.
The book also contains a number of Python labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.
Pages in this article:
Page 1: Understanding Machine Learning and more books
Page 2: Foundations of Data Science and more books
Page 3: Automated Machine Learning and more books