Data continues to inhabit every facet of human existence and so the need for competent Data Scientists to help leverage the insights from that data will invariably increase for the foreseeable future. According to a past EMC Data Scientist Study and the 2015 Global IT Report, the amounts of data created by the year 2020 will be upwards to 44 times what they were in 2009. Data Scientists use Machine Learning (ML) skills to develop powerful algorithms to make sense of the avalanche of data. Thus, Data Scientists with superior Machine Learning skills will be the transformative heroes of the digital world. Machine Learning teaches computers to conduct particular tasks like pattern diagnosis and recognition, planning, or prediction without the presence of any programming control ML generates "algorithms" that turn into self-teaching entities when exposed to data.

SAN FRANCISCO – Alphabet Inc.'s Google on Tuesday announced a new Pixel smartphone and a virtual reality headset, making a concerted move into home electronics and challenging Apple Inc.'s iPhone at the high end of the more than 400 billion global smartphone market. The string of announcements, including the 649 Pixel, a new Wi-Fi router and its voice-activated digital assistant for the living room, Home, is the clearest sign yet that Google intends to compete directly with Apple, Amazon.com and even its own Android mobile operating system customers to create a world of integrated devices and services that meet every digital need. The moves also show Google is taking tighter control of its products. Google lets Android device makers modify software almost without limit, which has helped make Android the most-used mobile operating system in the world. But company executives boasted that the Pixel was developed in-house from start to finish and gives it a direct hand on a platform for the distribution of its internet-based services.

As recently as 2013, the [deep learning] space saw fewer than 10 deals. Computer Vision: Startups here are using deep learning for image recognition, analytics, and classification. Aerial image analytics startup Terraloupe was seed-funded this year by Germany-based Bayern Kapital. New York-based Calrifai -- backed by investors including Google Ventures, Lux Capital, and NVidia -- entered the R/GA accelerator this year, after raising 10M in Series A in Q2'15. Captricity, which extracts information from hand-written data, has raised 49M in equity funding so far from investors including Social Capital, Accomplice, White Mountains Insurance Group, and New York Life Insurance Company.

Artificial intelligence, once restricted to NASA and sci-fi movies, is gradually becoming a prominent buzzword in the world of marketing technology. However, the widespread adoption of artificial intelligence in marketing is likely several years away as its application is not yet fully developed. But innovations taking place now are setting the stage for artificial intelligence and, more specifically, machine learning, to become a marketing standard in the near future. Many use the two terms of artificial intelligence and machine learning interchangeably; because they are so closely related, it is difficult to establish clear boundaries between them. While artificial intelligence refers to technology that has the cognitive capabilities to solve problems, machine learning allows marketers to learn and understand, among other things, how content is consumed, and helps them predict consumer behaviors based on data patterns and algorithms.

Google is taking a page from Apple's playbook by making a bigger push to build its own hardware. Rick Osterloh, head of Google's new hardware group, said that in doing so, Google can take full advantage of capabilities it's designing with artificial intelligence and machine learning. Apple has long designed both iPhone hardware and the iOS operating-system software that runs on it. Now, Google is doing the same with the upcoming Pixel phones running Google's Android system. The Pixel is one of several gadgets Google announced Tuesday in San Francisco.

Healthcare organizations are struggling to find ways to manage the risks of massive data breaches, which have proven hard to detect, often taking months to discover. In 1996 the Health Insurance Portability and Accountability Act (HIPAA) was enacted. The Accountability portion of the law requires that healthcare providers protect the privacy of patient health information and includes security measures that must be followed. Provider success has been mixed and has recently come under intense scrutiny due to the number and size of reportable breaches of health information. There are several major contributors to this increase. The first is the passage of the American Recovery and Reinvestment Act of 2009.

This project uses deep learning to upscale 16x16 images by a 4x factor. The resulting 64x64 images display sharp features that are plausible based on the dataset that was used to train the neural net. Here's an random, non cherry-picked, example of what this network can do. From left to right, the first column is the 16x16 input image, the second one is what you would get from a standard bicubic interpolation, the third is the output generated by the neural net, and on the right is the ground truth. As you can see, the network is able to produce a very plausible reconstruction of the original face.

Adaptive behavioural analytics software reduces'genuine transactions declined' by over 70% and incidence of undetected fraud by 25% Oakhall, the London based analysis firm, estimates that global financial services firms could save at least 12 billion annually by employing adaptive, machine learning fraud management systems according to a study published in conjunction with Featurespace. For the full study see http://www.featurespace.co.uk/cost-of-card-fraud. By employing adaptive behavioural analytics software to both identify actual fraudulent transactions, and reduce the number of'genuine transactions declined' - as well as reducing the costs associated with managing blocked customers - the industry could reduce the 31 billion total annual cost of card fraud by over 12 billion annually. Featurespace is a world leader in adaptive behavioural analytics software. Its services and products are employed in over 180 countries via a wide range of customers, including the leading US payments processor, TSYS, as well as Vocalink/Zapp, William Hill and Betfair.

This program offers dual career track such that the candidates enrolling this program have the option of choosing to become a data scientist or a data engineer. This program relishes an amazing support of industry stalwarts. The class size happens to be relatively small which allows the instructor to pay attention to every candidate.

Take part in this CrowdChat, hosted by @IBMBigData, to explore Apache Spark's powerful machine learning capabilities while taking a look at the future of Spark. When you do, you'll be able to interact with subject matter experts as they focus on Spark-related technologies and trends, including SystemML and structured streaming. If this event will be your first experience with CrowdChat, don't worry--a CrowdChat merely organizes tweets into streams of conversation. We'll start a conversation thread, and everyone who wants to participate can do so simply by commenting, allowing us all to enjoy an engaging discussion. Vote for your favorite comments so that they can be featured prominently in the conversation.