You can find many additional references here (Python, Excel, Spark, R, Deep Learning, AI, SQL, NoSQL, Graph Databses, Visualization, etc.) as well as here, here, and here.

Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers. Banks, telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics (along with cash flow, EBITDA, etc.) because the cost of retaining an existing customer is far less than acquiring a new one. Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients. Churn prediction is one of the most popular Big Data use cases in business. It consists of detecting customers who are likely to cancel a subscription to a service.

Most major technology companies are knee-deep in machine learning these days. Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Google, Amazon, and Facebook (NASDAQ:FB) are just a few. Machine learning allows the tech companies' computers to learn information on their own that they weren't programmed to know. For example, Google uses its own TensorFlow machine learning systems for its Google Translate speech recognition app, Google Photos, Gmail, and its Web searches. And as these companies dive further into machine learning, they're building their own complex computers using graphics processing units (GPUs) to power them -- and that could be particularly beneficial for NVIDIA (NASDAQ:NVDA).

A recent release out of the company reports, "AU10TIX, the forerunner of 2nd generation ID authentication and fast-track onboarding is already serving many global major players in a range of financial services and other verticals. The newly announced patented algorithms increase the capability and performance gap between 1st and 2nd generation'ID authentication' technologies. While 1st generation solutions rely primarily on MRZ or barcodes for document recognition and focus mainly on data based forgery detection, 2nd generation technology can carry out forensic level image manipulation and synthetic forgery checks, and can handle sub-optimal image qualities at far better speed and accuracy, at 100% data-entry-free automation."

The release goes on, "Under a scientific plan endorsed by 35 of the world's leading vaccine scientists, the Project aims to apply recent advances in machine learning and data science to speed vaccine development. 'Applying machine learning should accelerate the development of new vaccines and therapies for a wide range of pressing diseases,' said UC San Diego computer scientist and physicist Larry Smarr, director of Calit2. 'In addition to machine learning, experts in Big Data analytics, pattern recognition, genomics and bioinformatics can also help us understand the fundamental principles of effective human immunity, and enable the design of highly-targeted vaccines'."

Are you on to the next big thing? Does your product have the potential to change lives everywhere? If your software startup is working on an innovation or a brilliant idea, then this might be your first, big opportunity to connect with IDG Ventures India, a leading Indian, early-stage, venture capital firm. IDG Ventures India has launched the Software Innovators' 2016 programme to provide assistance to startups across the software products space. Sudhir Sethi, Founder Chairman, IDG Ventures India, says, "The Software Innovators Program looks to discover disruptive software product companies from India, with a potential to build a global foot print in the machine learning, artificial intelligence, SaaS, fintech and security areas amongst others. This program follows our successful software investments, including, Manthan, Newgen, Fintellix, Unbxd, and Uniphore."

As a technical founder at Siri, Sentient, and Viv Labs, Adam Cheyer has helped design and develop a number of intelligent systems. Drawing on specific examples, Adam reveals techniques he uses to maximize the impact of the AI technologies he employs.

DETROIT/SAN FRANCISCO (Reuters) - Concerns raised by the first reported fatality in a semi-automated car were expected to speed adoption of more sensitive technology to help vehicles see and drive themselves safely, increasing demand on the emerging autonomous vehicle technology industry, investors and analysts said. Goldman Sachs forecasts the market for advanced driver assistance systems and autonomous vehicles will grow from about 3 billion last year to 96 billion in 2025 and 290 billion in 2035. More than half of that revenue in 20 years, Goldman estimates, will come from radar, cameras and lidar, a sensor that uses laser – all tools considered essential to building vehicles that can pilot themselves. The May 7 death of Ohio technology company owner Joshua Brown in a Tesla Motors Inc Model S while the car's semi-automated Autopilot system was engaged highlighted the limitations of current automated driving systems. Tesla's Autopilot system uses cameras and radar, but not lidar.

Global solution provider Tech Mahindra has placed bets on the Internet of Things and machine learning platforms, most notably with its artificial intelligence platform, Tactix. The company calls Tactix its "flagship platform" for predictive analytics and automated resolution. Tech Mahindra's Ritesh Shah said Tactix offers simplified onboarding for SMBs looking for a start in artificial intelligence and machine learning. SMBs can easily log in and upload their existing data and information in the Expert Console. "When you talk about any cognitive application, or any intelligence solutions, training is the critical aspect and Tech Mahindra Tactix comes with a simplified version called Expert Console," Shah explained.

A recent release out of Insilico Medicine reports, "At Mensa Annual Gathering 2016, the annual event of the largest and oldest high IQ society transpiring in San Diego from June 29th to July 3rd, Dr. Michael West, CEO of BioTime, Inc announced the launch of a beta version of Embryonic.AI, an artificially intelligent system for analyzing the embryonic state of human cell samples using gene expression data. The first implementation of Embryonic.AI was launched by LifeMap Discovery, Inc, a subsidiary of BioTime, Inc and is freely available for beta testing. Scientists and companies from all over the world are welcome to test their stem cell and adult cell samples using Embryonic.AI and discuss collaboration alternatives.