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.

Jeff Bezos (R), founder and Chief Executive of Amazon.com and owner of The Washington Post, participates in a conversation with Martin Baron (L), executive editor of The Washington Post, during the event'Transformers: Pushing the Boundaries of Knowledge,' May 18, 2016 in Washington, DC. (Photo: Alex Wong/Getty Images) Artificial intelligence (AI) is perhaps one of the most debated and most controversial technologies today. In fact, experts have opposing opinions when it comes to the remarkable advancement and progress of AI technologies. Over the past decade, people have witnessed how the media depicted the rise of artificial intelligence in numerous science-fiction films. But according to AI experts and researchers, the disturbing possibility of artificial intelligence going out of control or turning evil is a thing of the past, citing the fact that AI is only capable of performing very specific tasks. Despite several explanations and discussions regarding artificial intelligence's potential to outwit humans by automating cognitive tasks, the rapid development and evolution of AI have led to concerns over safety and widespread unemployment.

This book is very easy to read and understand. Unlike Hastie's Statistical Learning book, it is not geared towards those with an expert level knowledge of statistics, and instead takes time to explain functions and formulas for the person with a decent but not extrordinary understanding of statistical/math concepts. For example, their description of a Gaussian was the clearest I've seen. On the other hand, if you're math/statistics background is considerable, you may find this book somewhat simplistic or tedious. The book has a good coverage of techniques and algorithms, although I was somewhat disappointed that they do not mention Influence Diagrams, considering the amount of coverage of both decision trees and Bayesian techniques.

This is an excellent book which is very accessible to readers in several fields. It gives a very good summary of different statistical techniques which are used for data mining. It also gives some good large data sets and show how the tools can be implemented. It begins with a chapter on summarizing the data to have an initial feel about the data. Then it gives discussion on regression (linear, polynomial, nonparametric).

Additionally, the fraud detection and prevention programs that keep your bank account safe are also utilizing predictive analytics. The most sophisticated of these programs analyze your spending habits and compare each purchase against them. The purchases themselves also have fraudulence probability scores (a 1,000 online purchase paid to a company based in Timbuktu is more likely to be fraudulent than a 200 purchase at your local grocery store). If anything seems fishy, the bank sends you an alert to ensure your funds aren't compromised if fraud is, in fact, taking place.

"No Dr., or How I Learned to Stop Debugging and Love the Robot" In this talk, Guy will dicuss what developers must know to explore the power of machine learning services in the cloud. Using data to build machine learning models is a powerful alternative for heuristic or handwritten rules. This power is not limited to people with Ph.D. or MSc. in machine learning, statistics or computer science, but can be used successfully by competent developers. You will learn how to get started and how to think in machine learning terms when developing your next smart application. To gain background on machine learning in the cloud before the meetup, consider reading Guy's blog posts on machine learning.