Data Mining: Practical Machine Learning Tools and Techniques
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Data Mining: Practical Machine Learning Tools and Techniques

3.73 of 5 stars 3.73  ·  rating details  ·  187 ratings  ·  20 reviews
"Data Mining: Practical Machine Learning Tools and Techniques" 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...more
Paperback, 629 pages
Published January 20th 2011 by Morgan Kaufmann Publishers (first published January 1st 2001)
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Todd Nemet
This is an excellent, but somewhat uneven, introduction to the field of machine learning, divided into three parts.

Part 1 is a good overview of the types of use cases, standard data sets, and algorithms. It provides more intuitive rather than technical explanations, though there is some math to get through. Reading just this section will definitely get you through any dinner party conversations about machine learning. I read through this twice, taking careful notes in my Moleskine (natch) the se...more
Derek Bridge
A useful compendium of data mining techniques and accompaniment to the Weka data mining tool. This book is more an overview than a detailed treatise: there are descriptions but few precise algorithms; the maths is kept to a minimum and, where there is maths, it is often left mostly unexplained; the applications seem dated - there's little on mining large-scale scientific, medical or web data, for example; and issues of handling large scale data are skirted. Nevertheless, its scope is wide and it...more
From the perspective of a computer scientist, this book is basically totally useless, as it leaves the reader with no idea how any of the algorithms really work. It might be helpful if you want to be able to use some machine learning software while avoiding having anything more than a cursory understand of how it works.
Best introductory book on Data Mining in terms of concepts and practice. Not too academically but goal-driven and data-driven, which makes readers understand it easier.

WEKA is a great tool, although its part in this book is a little bit too much.

For those who needs more on theory perspective, I recommend the book "Introduction to Data Mining" (Pang-Ning Tan, Michael Steinbach, Vipin Kumar). But if you want to apply it on business without knowing a lot of mathematical backgrounds, you can look fo...more
I really, really wanted to like this book more than I did. After all, it was about a topic that I have great interest in, and describes a workbench application (Weka) that I can command-line access from my favorite programming environment (R, via RWeka).

The problem I was having with it is that its presentation, across the board, was incredibly wordy. They managed to make the interesting sound boring, and presented technical material with no grace whatsoever. The chapter on the Weka Explorer was...more
Robert J.
While this book is an excellent overall summary of data mining technology, and it's an indispensable reference for using the Weka data mining software, it is not detailed enough, nor does it have enough examples, for an otherwise inexperienced novice data miner to be effective. If you come at it knowing a lot about statistics, probability, and modeling, you can get your knowledge rounded out with techniques and ideas you may not have experienced but make sense to you. If you don't bring such kno...more
John Orman
This big book has many sections that I used for my current Machine Learning online class: Applications, Knowledge Representation, Algorithms, Linear/Logistic Regression, Prediction, Classification, Clustering, and Cost Calculation. It also introduced me to the WEKA machine learning workbench, a set of free software tools that can be downloaded to implement many of the algorithms used in machine learning.
Brett Dargan
Loved this book. Although some parts were too slow, especially the first few chapters. Took a long time to explain concepts that could have been reduced a lot.
It is well worth sticking with it though; learnt some important concepts about data structures I hadn't come across before.
Ayman Sieny
The book provides a good introduction to data mining algorithms including classification, clustering and association. It also provides practical hands-on exercises using an open source data mining tool developed by the authors called WEKA.
Juliusz Gonera
Very hands on/practical intro to the subject. For readers who want to start using ML techniques quickly and worry about theoretical considerations later.
Bill Hayes
May 13, 2012 Bill Hayes is currently reading it  ·  review of another edition
Shelves: technical
I like his stated approach to give readers a good feel for the different techniques of Machine Learning and what they can be used for.
I was looking for something not so theoretical, which is totally what it was to me. Practical to me means something with code...
Soren Macbeth
A good medium level introduction to data mining. Written by the authors of WEKA which is used to apply the concepts in the book.
Timon Karnezos
Pedantic to a fault. Otherwise, it's just a bunch of algorithms with analysis and discussion.
Jul 21, 2011 Kurt marked it as to-read
Very mathy and deep, but also seems very practical and actionable so far.
Explains various ML schemes very well but limits only to WEKA.
Ayoola Adegbite
recommended , used for data mining course at uni ..quite practical
Pretty darn good in terms of applied data-mining.
Alon Gutman
Love the tool(Weka) the book is bad.
Somkiat Chatchuenyot
Good to begin for web mining
Dompuiu marked it as to-read
Jul 19, 2014
Iulian Dumitru
Iulian Dumitru marked it as to-read
Jul 13, 2014
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Jul 10, 2014
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