I would recommend these machine learning books
Published:
- Machine Learning Yearning by Andrew Ng. Artificial Intelligence is transforming numerous industries. Machine Learing Yaerning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to:
- Prioritize the most promising directions for an AI project
- Diagnose errors in a machine learning system
- Build ML in complex settings, such as mismatched training/ test sets
- Set up an ML project to compare to and/or surpass human- level performance
- Know when and how to apply end-to-end learning, transfer learning, and multi-task learning.
Statistical Inference for Data Science by Brian Caffo. Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. This book gives a brief, but rigorous, treatment of statistical inference intended for practicing Data Scientists.
Think Stats by Allen B.Downey. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You’ll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you’ll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.
Understanding Machine Learning: From theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David. I have read many of the main books on machine learning. This is hands down the best. Rather than a laundry list of techniques, the book starts with a concise and clear introduction to statistical machine learning and then consistently connects those concepts to the main ML algorithms. Each chapter is 10 pages or so of crisp math and lean prose. A brief summary at the beginning of each chapter gives a clear sense of what will be accomplished in it, and attention to notation makes sure that mathematics supports understanding rather than getting in the way. This is definitely not a “how to” book, but rather a “what and why” book, focused on understanding principles and connections between them. I read the book cover to cover, and I was left with a sense of machine learning as a coherent discipline and a solid feel for the main concepts.
The Elements of Statistical Learning By Trevor Hastie, Robert Tibshirani and Jerome Friedman.
- Foundations of Data Science By Avrim Blum, John Hopcroft, and Ravindran Kannan.This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
