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Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems)

Ian H. Witten, Eibe Frank, Mark A. Hall · 3 HN comments
HN Books has aggregated all Hacker News stories and comments that mention "Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems)" by Ian H. Witten, Eibe Frank, Mark A. Hall.
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Amazon Summary
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.
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Hacker News Stories and Comments

All the comments and stories posted to Hacker News that reference this book.
sure, there's 2 sides, the maths and the programming/systems

________

the math is mostly the same math that physics and EE majors do for first 2 years, plus probability/stats:

https://www.seas.harvard.edu/courses/cs281/#schedule

http://www.zipfianacademy.com/blog/post/46864003608/a-practi...

http://fastml.com/math-for-machine-learning/

________

the general purpose programming languages mostly used are python, C++, java, and the statistical languages include R, matlab.

there's lots of good threads at /r/machineLearning, including

http://www.reddit.com/r/MachineLearning/comments/2i68f0/tack...

http://www.reddit.com/r/compsci/comments/2jw14w/which_fields...

also some books for people who haven't the rigorous math background yet are Intro Statistical Learning (James et al), the ones by Peter Flach, Steven Marsland, and Witten/Franke:

http://www.amazon.com/Data-Mining-Practical-Techniques-Manag...

http://www.amazon.com/Machine-Learning-Algorithmic-Perspecti...

⬐ med_guy
This is very helpful. Thanks a lot!
⬐ gtani
If you can get through Organic and P. Chem, you can get thru this stuff!
We use this book for statistical models and data mining, combined with econometric models (GMM, 2LS)

http://www.amazon.com/Data-Mining-Practical-Techniques-Manag...

If you want a "less math" machine learning book, I like these two:

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition

Ian H. Witten, Eibe Frank, Mark A. Hall

http://www.amazon.com/Data-Mining-Practical-Techniques-Manag...

Machine Learning: An Algorithmic Perspective

Stephen Marsland

http://www.amazon.com/Machine-Learning-Algorithmic-Perspecti...

⬐ None
None
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