All the core content from the text rewritten in bulletized form for cut-to-the-chase mastery of the subject. Includes all objective testable terms, concepts, persons, places and events in browser based e-book format. Not just the facts, but interactive problem solving labs to ensure you master the concepts as well. Lab tools allow for thread-like collaboration among classmates and friends. Includes pre-made flashcards, and practice tests in true or false, multiple choice, mastery, or completion formats.

Siamak Amirghodsi (Sammy) is a world-class senior technology leader with an entrepreneurial track record of overseeing big data strategies, analytics, data platforming, enterprise architecture, technology road mapping, multi-project execution, and organizational streamlining in Fortune 20 environments. Siamak is an early big data adapter and is currently overseeing the fast growing FX payments analytics and data platform build out for a tier-1 investment bank in the United States. Siamak's interests are big data, Hadoop, Spark, streaming systems, deep machine learning, cognitive models, google brain project, swarm algorithms, quantum computing, trading signal discovery, financial cycles, cryptography, digital/virtual currencies, probabilistic graphical models, and NLP.

MasterCard (MA) is taking payments, the last -- and sometimes, the most painful -- part of shopping into the future, replacing card swipes with robots, artificial intelligence and the ubiquitous selfie. The steps, including this week's introduction of a chatbot for banks called "MasterCard KAI" that uses artificial intelligence to respond to customer queries via texts or through apps like Facebook Messenger, are vital parts of CEO Ajay Banga's strategy of leverage technological development to expand the $113 billion company beyond traditional card-based transactions. To create the chatbot, MasterCard partnered with startup Kasisto, developing a "conversational artificial intelligence platform" that banks and merchants can use to let customers make transactions, monitor their spending habits, check account balances and ask questions, the company said at the Money 20/20 conference in Las Vegas. It will be released in the U.S. early next year. "This bot enables entirely new experiences, bringing Mastercard benefits and offers to consumers with human-like conversations that are personal and contextual," Zor Gorelov, Kasisto CEO and co-founder, said in a statement.

Facebook recently announced a rather scenically named system, Big Sur, designed around Nvidia's Tesla compute cards aimed at helping their neural networks, and obviously machine learning, become faster and more versatile. They are, of course, not alone. IBM Watson has similar visions as does Microsoft. Big Data and analytics have long staked claim to the holiday shopping season, but I sense that this 2015 holiday shopping season the real big winner will be Machine Learning. The big gun in the Machine Learning camp is the aforementioned IBM Watson.

The text is supported by a strong outline. The authors preserve much of the introductory material, but add the latest techniques and developments in data mining, thus making this a comprehensive resource for both beginners and practitioners. The focus is data-all aspects. The presentation is broad, encyclopedic, and comprehensive, with ample references for interested readers to pursue in-depth research on any technique. "This interesting and comprehensive introduction to data mining emphasizes the interest in multidimensional data mining--the integration of online analytical processing (OLAP) and data mining. Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers."

Michael Brzustowicz is a physicist turned data scientist. After a PhD from Indiana University, Michael spent his post doctoral years at Stanford University where he shot high powered Xrays at tiny molecules. Jumping ship from academia, he worked at many startups (including his own) and has been pioneering big data techniques all the way. Michael specializes in building distributed data systems and extracting knowledge from massive data. He spends most of his time writing customized, multithreaded code for statistical modeling and machine learning approaches to everyday big data problems.

Apple Inc. hired a prominent artificial intelligence researcher from Carnegie Mellon University as it seeks to regain lost ground against competitors such as Google, Microsoft Corp. and Amazon.com He posted a link to an Apple job application page seeking machine learning specialists. Apple is seeking scientists with "experience in Deep Learning, Computer Vision, Machine Learning, Reinforcement Learning, Optimization, and/or Data Mining," it said in the job listing. So you can sleep an extra five minutes. Travel with us, drive with us, eat with us – around the world.

John Lewis [IRDX RJLW] is investing in retail tech startups working in areas from machine learning to social media following the completion of its latest JLAB accelerator programme. The retailer, an Elite trader in IRUK Top500 research, and its innovation partner L Marks will together put 100,000 into DigitalBridge, a technology company that uses computer vision and machine learning technology to enable customers to see how new home furnishings will look within their homes. Wedding Planner, which enables couples to plan their wedding over their phones and online, and Link Big, whose technology turns Instagram into a social checkout, enabling customers to buy products seamlessly from their Instagram feed shop, both receive 50,000. The John Lewis Buying teams will continue working with the two other startups on JLAB 2016, Ding Labs and Robotical, with a view of helping to bring their products to market. The five startups were part of JLAB 2016, a ten week programme working within John Lewis operations to put their technology to practical use.

Dr. Anil Maheshwari has done a great job in taking a complex, highly important subject area and making it accessible to everyone. The book begins by simply connecting to what you know, and then bang - you've suddenly found out about Decision Trees, Regression Models and Artificial Neural Networks, not to mention cluster analysis, mining and Big Data. It takes real experience and authority, as well as a great generosity of spirit to be able to write like this. For instance in Chapter 12 on Big Data, Dr. Maheshwari presents "The Big Data Landscape" that gives you a great overview on a single page. I also much appreciated the way the Primer sections are made available, for beginners, and there is also a way for more advanced readers to get to stuff that is useful to take them to the next level.

Some retailers have been utilizing artificial intelligence (AI) for a while now while others are merely dabbling in it. Sure, AI is much louder and more obvious in the mainstream media or in our everyday lives (think the onset of self-driving cars) than it is in retail. But AI applications can help retailers digitally integrate with physical stores and provide richer, more engaging experiences with their customers. Assuming customer engagement is your goal, it's time to get serious about AI.