"This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time going through a formal degree program."--Deepak Agarwal, VP of Artificial Intelligence at LinkedIn "This book is a great introduction to machine learning from a world-class practitioner and LinkedIn superstar Andriy Burkov. He managed to find a good balance between the math of the algorithms, intuitive visualizations, and easy-to-read explanations. This book will benefit the newcomers to the field as a thorough introduction to the fundamentals of machine learning, while the experienced professionals will definitely enjoy the practical recommendations from Andriy's rich experience in the field."--Karolis

What the Amazon founder and CEO wants for his empire and himself, and what that means for the rest of us. Where in the pantheon of American commercial titans does Jeffrey Bezos belong? Andrew Carnegie's hearths forged the steel that became the skeleton of the railroad and the city. John D. Rockefeller refined 90 percent of American oil, which supplied the pre-electric nation with light. Bill Gates created a program that was considered a prerequisite for turning on a computer. At 55, Bezos has never dominated a major market as thoroughly as any of these forebears, and while he is presently the richest man on the planet, he has less wealth than Gates did at his zenith. Yet Rockefeller largely contented himself with oil wells, pump stations, and railcars; Gates's fortune depended on an operating system. The scope of the empire the founder and CEO of Amazon has built is wider. Indeed, it is without precedent in the long history of American capitalism. More product searches are conducted ...

Robotic Process Automation (RPA) has been around long enough now that industry-specific flavours have become mature, scalable models. Unlike in the earlier years of RPA, when implementations could fail because of poor or limited compatibility between different systems, RPA technology solutions these days are capable not only of working seamlessly with every system that's already in place but also of adapting to changes in environments caused by operational, legal, regulatory and other factors. In fact, it is expected that over 40% of the world's biggest corporations will implement RPA solutions by 2021. In complex industries that deal with as many processes as there are'moving parts', such as retail, banking, insurance, healthcare and manufacturing, RPA is proving to be a major game-changer with its impact on key parameters. Automation drives up efficiency and productivity; it also results in lower operational expenditure can that offset the higher initial costs within as low as 2 years.

What does the future hold for us? Or more precisely, how will the future unfold? One thing that we know unanimously is that the digital revolution is here. This revolution is cascading across every industry and organization, resulting in wide-scale enterprise disruption with redefined customer expectations. The ability to rapidly rotate to the new has been critical for companies striving to become digital leaders, as well as for the employees who are shifting beyond the digital culture shock.

A practitioner s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field recommendations are provided for further study in order to build advanced skills in tackling important data problem domains.The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work.

Today's hottest deals include a Roomba and a pair of true wireless headphones. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. Online shopping can be both the best of times, and the worst of times. It's always a thrill to find a product that you've been eyeing finally go on sale, but then the fear can creep in that maybe you can find it for a lower price somewhere else on the web, or there's a promo code you're missing out on, or maybe the retailer jacked up the original price just to trick you.

Competition to build retail mindshare and customer loyalty has never been more fierce. Brands must continually evolve their products and deliver a personalized approach to win the hearts and minds of customers. Delivering the technology that helps them do exactly that, Portugal Country Leader Bruno Morais, and Mike Webster, senior vice president and general manager Oracle Retail, unveiled a new Innovation and Technology Center in Porto. The center will focus initially on delivering breakthrough retail innovations leveraging the latest technologies, including artificial intelligence and machine learning. "Oracle Portugal is delighted to see Oracle's Global Retail business unit's commitment to and investment in people and technologies in Porto. Similarly, we are delighted to contribute to the development of Porto through the creation of this global technology and innovation hub," said Morais.

Chiniki Guard, a Nigerian Information Communication Technology (ICT) startup application has won the best prize of 10,000 USD in the Artificial Intelligence category competition at the ongoing Gulf Information Technology Exhibition (GITEX). GITEX is a global annual and biggest technology gathering in the Middle East,North Africa and South Asia with theme "Synergising the Mind and Technology Economy."aqq Chiniki Guard is an Artificial Intelligence security solution for retail stores,supermarkets to monitor,detect and alert shop owners of shoplifting as well as suspicious behaviour in real time. Chiniki Guard is among the 34 startups in the category of the Supanova finalists at the GITEX Futurestars competition. Speaking with the News Agency of Nigeria (NAN)Mr Abdulhakim Bashir,the Founder of Chiniki Guard, said the opportunities at GITEX had accorded him the win and would enable him widen the scope of his business.

One Drop, a New York City-based provider of digital therapeutic solutions for diabetes and other chronic conditions, today announced that its wireless glucose meter kit and supplies are now available at hundreds of Walmart stores in the United States. Each kit includes a Bluetooth glucose meter, lancing device, carry case, test strips, and lancets, as well as one year of free access to One Drop's certified diabetes coaches for 24/7 support. In addition to being sold at US Walmart stores and on walmart.com, One Drop's wireless glucose meter is currently available at select Apple retail locations, and on getonedrop.com, One Drop's integrated platform offers an easy and effective way for people to track and manage a variety of health conditions.

Online retail, eCommerce, frequently falls victim to fraud conducted by malicious customers (fraudsters) who obtain goods or services through deception. Fraud coordinated by groups of professional fraudsters that place several fraudulent orders to maximize their gain is referred to as organized fraud. Existing approaches to fraud detection typically analyze orders in isolation and they are not effective at identifying groups of fraudulent orders linked to organized fraud. These also wrongly identify many legitimate orders as fraud, which hinders their usage for automated fraud cancellation. We introduce a novel solution to detect organized fraud by analyzing orders in bulk. Our approach is based on clustering and aims to group together fraudulent orders placed by the same group of fraudsters. It selectively uses two existing techniques, agglomerative clustering and sampling to recursively group orders into small clusters in a reasonable amount of time. We assess our clustering technique on real-world orders placed on the Zalando website, the largest online apparel retailer in Europe1. Our clustering processes 100,000s of orders in a few hours and groups 35-45% of fraudulent orders together. We propose a simple technique built on top of our clustering that detects 26.2% of fraud while raising false alarms for only 0.1% of legitimate orders.