My current work revolves around using machine learning for the study of criminal behavior, so I read Introduction to Machine Learning with Python by Andreas Muller and Sarah Guido with great interest. The book comprises a complete documentation of the scikit-learn library, and provides a comprehensive overview of the machine learning models and the fundamental theory needed to get started in applying ML tools in practice. Each chapter contains Python source code that cover a wide range of interesting and practical data science problems. In addition to the basic theory, scikit-learn tools and code samples, the book also includes many useful hints, tricks and words of wisdom that can save you a lot of time by avoiding issues that invariably arise in your learning process. This is an excellent book that I highly recommend both to machine learning experts who want to be proficient in scikit-learn and also to beginners who want to learn machine learning basics and how to apply them on data.

Last week a story went viral about how Facebook shut down two artificial intelligence robots when they started talking to each other in a language engineers couldn't understand. Welcome to the intelligence era. Let's think a little deeper about AI than the rise of the machines and consider the next big industrial shakeup. Artificial intelligence and big data are going to change everything. Yet again, innovation will flourish and industries will be completely disrupted. New industries and industry leaders will emerge, and many companies will go out of business.

With the release of Core ML by Apple at WWDC 2017, iOS, macOS, watchOS and tvOS developers can now easily integrate a machine learning model into their app. This enables developers to bring intelligent new features to users with just a few lines of code. Core ML makes machine learning more accessible to mobile developers. It also enables rapid prototyping and the use of different sensors (like the camera, GPS, etc.) to create more powerful apps than ever. Members of the MXNet community, including contributors from Apple and Amazon Web Services (AWS), have collaborated to produce a tool that converts machine learning models built using MXNet to Core ML format.

Rodolfo Bonnin is a systems engineer and PhD student at Universidad Tecnologica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni Stuttgart, Germany. He has done research on high performance computing since 2005 and began studying and implementing convolutional neural networks in 2008,writing a CPU and GPU - supporting neural network feed forward stage. More recently he's been working in the field of fraud pattern detection with Neural Networks, and is currently working on signal classification using ML techniques.

Every brand and vendor performs some kind of demand forecasting. In the last few years, data science has gone massively mainstream, and new machine-learning techniques have dramatically improved the accuracy of forecasting in the supply chain. The impact has been pretty stark in retail, where a three per cent improvement in forecast accuracy translates to an average two per cent increase in profit margin for the retailer. By putting groups of algorithms to work on forecasting's knottiest problems, some of the most notoriously unpredictable demand patterns have been cracked – and that's had an unexpected side-effect: some companies have started applying these forecasting techniques in novel and unexpected ways, particularly product marketing. To understand why, we need to look at what's previously been out of reach to forecasters.

As digital giants swallow a growing share of shoppers' spending, thousands of stores have closed and tens of thousands of workers have lost their jobs. Belinda Duperre, who sold jewelry at Sam's Club in Fall River, Mass., was one. In early 2016, the struggling store closed. But Ms. Duperre, a lifelong resident of the once-thriving factory town an hour south of Boston, went from victim of the digital revolution to beneficiary. Inc. announced plans to hire 500 full-time workers for a new 1.2-million square foot fulfillment center on the outskirts of town.

Since 2007, 140,000 brick-and-mortar retail jobs have vanished in America. Meanwhile, according to the Bureau of Labor Statistics defintions, e-commerce has created just 126,000 over the same perioud. The takeaway, it seems: automation, here in the form of the software and robots that power online retail, is eating jobs. But according to a new analysis by the Progressive Policy Institute, those figures miss the point. If you actually include all the fulfillment-center jobs that e-tail has created, which wouldn't have otherwise needed to exist, the figure rises from 126,000 to 400,000, far outweighing physical-store losses. And those fulfillment-center jobs also pay on average 31 percent more than brick-and-mortar store jobs would in the same county.

There was a time, not too long ago, when Wal-Mart (NYSE:WMT) was the undisputed king of retail. However, e-commerce has changed the landscape, and over the last few years, Amazon.com, Inc. (NASDAQ:AMZN) has been taking an increasingly large slice of the retail pie. Wal-Mart has been playing catch-up in online sales, but now it seems the retail giant is ready to take the fight to Amazon, with a little help from NVIDIA Corporation (NASDAQ:NVDA) and artificial intelligence (AI). In a note to clients, Global Equities Research analyst Trip Chowdhry revealed that Wal-Mart would be building huge data centers to house its cloud computing and make a sizable push into deep learning, a segment of artificial intelligence.

Giancarlo Zaccone has more than ten years of experience in managing research projects both in scientific and industrial areas. He worked as researcher at the C.N.R, the National Research Council, where he was involved in projects relating to parallel computing and scientific visualization. Currently, he is a system and software engineer at a consulting company developing and maintaining software systems for space and defense applications. He is author of the following Packt volumes: Python Parallel Programming Cookbook and Getting Started with TensorFlow. Rezaul Karim has more than 8 years of experience in the area of research and development with a solid knowledge of algorithms and data structures, focusing C/C, Java, Scala, R, and Python and big data technologies such as Spark, Kafka, DC/OS, Docker, Mesos, Hadoop, and MapReduce.

The department store chain will house Amazon shops inside 10 of its stores. As Fred Katayama reports, the move comes as Kohl's tries to snap its streak of falling sales. Kohl's shoppers will be able to buy devices, connected to Amazon's voice assistant, directly from the e commerce giant starting next month. Online retailer Amazon is pushing deeper outside its digital realm by carving out spaces in Kohl's stores where customers will be able to buy direct from it. While Amazon devices have long been available at other traditional retailers like Best Buy, the deal with Kohl's is unique in that shoppers will browse a 1,000-square-foot space dedicated to Amazon.