Infor, a leading provider of business applications, has announced the acquisition of Predictix, a provider of machine-learning solutions for retailers. Predictix will become part of Infor CloudSuite Retail, a new suite of enterprise applications delivered in the cloud and designed for today's retailing landscape. The acquisition comes six months after Infor announced an investment in Predictix. "The synergies between Infor and Predictix were greater than we could have hoped, and we've come to appreciate a great cultural alignment where both teams have passionate people who work hard and want to make a difference in retail and beyond," said Charles Phillips, CEO of Infor. "Buying out the other Predictix investors makes sense to bring the teams together and provide the scale and resources needed to accelerate the retail revolution."

As databases keep growing unabatedly, so too has the need for smart data mining. For a competitive edge in business, it helps to be able to analyse your data in unique ways. This text gives you a thorough education in state of the art data mining. The extensive problem sets are well suited for the student. These often expand on concepts in the narrative, and are worth tackling.

Madhavan's book has proven useful for some of the projects I'm working on. The first chapter includes a brief primer on Numpy and Pandas--useful for someone that is new to the Python ecosystem, but assuming you are already familiar with those packages, it should be okay to skip to the second chapter. The second chapter includes some Python statistical examples that I have not seen in other texts, but are important when looking at different types of distributions. These distribution examples and explanations are a must-have in my collection of Python recipes. There are also data visualization tweaks that I've not seen in other Data Science Python texts.

The way we are used to do things and draw decisions is about to change with the oncoming digital evolution. Software and hardware are converging in the Internet of Things and devices driven by machine learning will bring in machine-like accuracy and speed to support data driven human decisions and actions. In today's world, we need to designs solutions for customer engagement in the digital age, and regularly rework our industry scenarios to stay ahead of the game. There is a call for design of data driven, machine learning enabled enterprise software to run on state-of-the-art connected devices. As the amount of data continues to grow, it is essential to utilize the power of deep learning to urge better decisions throughout the customer engagement process, and to run on the next generation of connected devices.

There are a few key points and reasons here. The first is that the book starts all the way from installing the tools and performing all of the basic coding that will be needed later in the book. This fundamentals in the first portion is something that many other books I have purchased did not cover. The book actually exposed me to a few new libraries that I found personally useful including a hands on approach with numl and accord.net,

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.