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Top Machine Learning & Data Mining Books - in this post, we have scraped various signals (e.g. reviews & ratings, topics covered in the book, author influence in the field, etc.) from web for more than 100 Machine Learning, Data Mining, and NLP books. We have combined all signals to compute a score for each book and rank the top Machine Learning and Data Mining books.
The readers will love the list because it is data-driven & objective. Enjoy the list:
This book is very well rated on Amazon website and is written by three professors from USC, Stanford and University of Washington. The book's authors: Gareth James, Daniela Witten, Trevor Hastie, & Rob Tibshirani all have backgrounds in statistics. The book is more practical than "The Elements of Statistical Learning" counterpart with presenting examples in R.
A well rated book on Amazon written by three statistician professors from Stanford. The first author is Trevor Hastie with research background in statistics & bio-statistics. One interesting thing about the book is that the authors' statistical view to machine learning problems. The book seems a bit heavy invested in theory, so some readers might prefer to pass it!
A highly rated book on Amazon written by a well-known author Christopher M. Bishop who is a distinguished Scientist at Microsoft Research in Cambridge where he leads the Machine Learning and Perception group. The book is technically comprehensive where it invested on various ML topics including Regression, Linear Classification, Neural Networks, Kernel Methods, and Graphical Models.
The "Machine Learning: A Probabilistic Perspective" book provides methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. The textbook offers a comprehensive introduction to the field of machine learning, based on a unified, probabilistic approach. The author of the book, Kevin Murphy, is a research scientist at Google where he works on AI, machine learning, computer vision, knowledge base construction and natural language processing.
The "Data Mining: Concepts and Techniques" book written by Jiawei Han from Department of Computer Science at Univ. of Illinois at Urbana-Champaign. The book equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets and has got an average review on Amazon.
This book is rated quite well on Amazon website. It's written by three computer science professors from University of Waikato in New Zealand. The author also were the main contributors of Weka - a data mining software written in Java. Thus, the book spent time on implementation side of data mining area specifically on Weka software workbench.
The Probabilistic Graphical Models: Principles and Techniques is a unique book providing a framework of probabilistic graphical models to design an automated system to reason. The book is written by two computer science professors: Daphne Koller from Stanford AI lab and Nir Friedman from The Hebrew University of Jerusalem.
The "Introduction to Information Retrieval" is written by compute science professor "Christopher Manning" from Stanford. This is a textbook that teaches web-era information retrieval, including web search and the related areas of text classification and text clustering from basic concepts.
The "Machine Learning" is a well-know book in the field of Machine Learning written by Tom Mitchell - an American computer scientist professor from the Carnegie Mellon University. Tom Mitchell is the first Chair of Department of the first Machine Learning Department in the World, based at Carnegie Mellon. The "Machine Learning" book touches a few fundamental areas in ML including: Learning, Decision Tree Learning, Neural Networks, Bayesian Learning, Reinforcement Learning and so on.
The "Speech and Language Processing" is written by Dan Jurafsky who is professor of linguistics and computer science at Stanford University. The first of its kind to thoroughly cover language technology – at all levels and with all modern technologies – this book takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations.
Well rated book on Amazon website. The book is written by three computer science professors: Pang-Ning Tan from Michigan State University, Michael Steinbach and Vipin Kumar both from University of Minnesota. The book covers different fundamental areas in data mining such as: classification, association analysis, clustering, and anomaly detection.
The "Foundations of Statistical Natural Language Processing" is a very well rated NLP book on Amazon. Statistical approaches to processing natural language text have become dominant recently. This foundational text is a comprehensive introduction to statistical natural language processing (NLP). The book contains all the theory and algorithms needed for building NLP tools.
This book is rated above average on Amazon website and is written by three PhD's who have industrial experience in the fields of data mining and statistics. The book is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers through different stages of data analysis, model building and implementation.
The "Understanding Machine Learning: From Theory to Algorithms" provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. The authors of the book are both computer science professor from the Hebrew University of Jerusalem and University of Waterloo.
The "Foundations of Machine Learning" is a graduate-level textbook introducing fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The author, Mehryar Mohri, is a professor of computer science at the Courant Institute of Mathematical Sciences at New York University.