The book is more like a quick compilation of a college student's note. Concepts are presented in a relatively isolated manner; connections between concepts are, for the large part, missing. Furthermore, if the materials presented are rather shallow like in this book, readers will expect to see a strong emphasis on, or hands on exercises of, practical applications. But this book doesn't seem to help much in that regard either, despite what the book claims. The book does give introduction to a bunch of models, which can be useful for a beginner. But at least this edition I wouldn't suggest any one to buy because of poor editing.

I plan to use this book as a supplement to an MBA-level course on Business Analytics. It is extremely well organized, clearly written and introduces all of the basic ideas quite well. It covers the fundamentals of "data mining" and "data visualization", classification and prediction, logistic regression, cluster analysis, and forecasting. It comes with a 6 month license to XLMiner which is an add-in to Excel and access to data for a number of cases. First of all, there are an incredible number of typos in the book -- far too many for a Second Edition.

There exists a couple of classics of Machine learning, with various strengths and weaknesses. I'd say this is the most practical of the three books. The other two I mentioned are oriented towards theoretical underpinnings, and cataloging the rich zoology of machine learning techniques. This one tells you how to get stuff done. It even has practical advice on things you really need an expert opinion on: for example, when using data folding techniques for cross validation ... what is a good number of folds to use? This book will tell you.

Managing Data in Motion, Data Integration Best Practice Techniques and Technologies is a really well written work that surveyed a broad range of practices and technologies used in Data Integration. The book avoided overburdening jargon so it should be quite accessible to anyone who is interested in learning about Data Integration's past, present and future. The author has done a valiant job pulling together a wealth of knowledge into an easily consumable form. In addition to her own words, I also greatly appreciated the sidebars from experts in their own domains. While the Table of Contents is somewhat intimidating in its list of topics, it was really a very easy read.

I'm a programmer with no great mathematical background (2nd year university maths and stats, decades old and mostly forgotten) trying to teach myself about machine learning, and I found this book to be at exactly the right level for me. It's strongly oriented towards classifiers of one sort and another, and makes no claims to cover neural nets, genetic algorithms, genetic programming - but what it does cover it covers exceptionally clearly. I'd give it six stars out of five if it covered all aspects of machine learning, but I guess I can't have everything. In terms of writing style and comprehensibility this is probably one of the best textbooks I have ever read. I wish that it covered much much more, but what it does do it does remarkably well.

Once I started reading the book, it was hard to put down. It slowly and gradually introduced big data and data analytics techniques. The ideas and approaches to natural language processing and machine learning are well written. These methods are rarely found together anywhere else. I learned a lot by reading this book and gained a lot of confidence when I talk about data analytics, which method we should use and when.

This book fills an important gap in large scale data science. Spark has emerged as the big data platform of choice for data scientists both from the ease of use as well as the performance / optimization point of view. In a few lines of Scala code, Spark allows you to write iterative algorithms that scale out very well. For a data scientist who wants to explore large scale data sets, Spark is a great starting point (this is incredible progress in the Spark community given the project is just about 4 years old). However, Spark itself is moving fast and maturing with time, and Spark and Scala as well as distributed algorithms are typically not in the arsenal of many data scientists today.

It's been a few years since we first met Five Elements Robotics at RoboBusiness, where they introduced Budgee, a sort of robotic stuff-carrier that will follow you around with up to 22 kilograms of your junk by homing in on a small ultrasonic emitter. Last week at the Bloomberg Technology Conference, Five Elements CEO Wendy Roberts announced that Walmart is evaluating a prototype of a new Five Elements robotic shopping cart called Dash. Dash is much more than an upgraded version of Budgee; it's a completely new platform, specifically designed for autonomous shopping assistance. There are a lot of things I like about this idea. There's a clear value proposition to a robot that can carry groceries, guide shoppers directly to the items they want, and then handle paying for those items, since people hate doing all of those things.

If you're interested in large scale machine learning, then this book is for you. This book doesn't provide deep coverage of theoretical foundations of machine learning (I would recommend to look to other books, like Introduction to Machine Learning (Adaptive Computation and Machine Learning series), Machine Learning in Action or Programming Collective Intelligence: Building Smart Web 2.0 Applications, etc., if you want to get more background), but concentrates on explanation on how to use Apache Mahout ([...]) to solve some of machine learning problems: making recommendations, data clustering & classification. For each of class of these problems, description starts with base things, and continues with more complex examples, including complete solutions, that could be easily adapted for your machine learning problems. All examples that come with book were checked with actual release of Apache Mahout (version 0.5). Book is written in succinct, but understandable language and provides many code snippets that make understanding of topics much easier.

Dr. Ratner has written a unique book that distinguishes between statistical and machine-learning data mining. The book includes 14 statistical data mining and 17 machine-learning data mining techniques. All techniques are quite practical, making this volume a handbook for every statistician, data miner, and machine-learner. Let me describe a few chapters that present approaches and techniques that I really favored. Chapter 3 introduces a new data mining method: a smoother scatterplot based on CHAID.