I teach a 5-step process that you can use to get your start in applied machine learning. The traditional way to teach machine learning is bottom-up. Start with the theory and math, then algorithm implementations, then send you off to figure out how to start solving real-world problems. The traditional approach to getting started in machine learning has a gap on the path to practitioner. The Machine Learning Mastery approach flips this and starts with the outcome that is most valuable.

Google wants to change how it relates to enterprise customers, and it's going to use Google Cloud to do so. Today at an event in San Francisco, it made announcements about machine learning, Kubernetes, and expansion of its Google Cloud Platform presence. But the bigger-picture news is that cloud will be taking a leading role at the company. Google is bringing together its massive Google Cloud Platform (GCP), along with a new application platform called G Suite (formerly Google Apps). And it's hiring 1,000 people to boost its cloud business. This is all under one big umbrella called Google Cloud.

Nick McClure is a senior data scientist for PayScale, where he works on machine learning and natural language processing algorithms. Prior to joining PayScale, he worked on the Zestimate team at Zillow and as a gaming statistician and data scientist at Caesars Entertainment in Las Vegas. He has worked on various topics, such as house price estimation, image recognition, casino game design, optimal slot machine placement, and predicting customer worth. He received a National Science Foundation Integrative Graduate Education and Research Traineeship fellowship for studying infectious disease while working on his master's and Ph.D. in applied mathematics from the University of Montana.

For Lou Carvalheira, advanced analytics manager in IT vendor Cisco Systems Inc.'s customer intelligence unit, which is also based in San Jose, machine learning has underpinned analytics for many years. That notion is so familiar that "it is not something we speak about anymore," he said. What is new in the quest to identify potential buyers, he continued, is that "we are finding ways to scale processing. Machine learning is empowered by the fact that you can now process much more data. You use a tremendous amount of computation power, not just one computer."

A'chat bot' is a computer programme designed to simulate an intelligent conversation with human users by way of text or telephone. RBS will use IBM's'Watson' - a platform that analyses unstructured data - to power its service, which will aim to be able to answer specific customer questions, such as'how do I authorise my card to be used overseas?'. In the case of more complex questions, the chat bot will direct customers to a human who can answer them. The bank said it had already tested the servicce among 1,200 RBS and NatWest staff over a two-month trial and now expects the AI bot - if it is successful in its customer pilot - to be rolled out to customers of both brands. In March, RBS announce it would let go 220 investment advisers as part of a cost-cutting drive that would see large parts of its face-to-face service replaced with telephone and online solutions.

You might not be campaigning to be America's next president, or have any desire to hold such a demanding office (bless you, Hillary), but wouldn't it still be nice to be treated like POTUS when you travel? Or, at least spend a few days in the presidential suite feeling like one of the world's most … Election jokes are i Saturday Night Live' /i s bread and butter, so it should come as no surprise that the cast took aim at Donald Trump's hot mic scandal. But host Lin-Manuel Miranda also got a chance to shine in his opening monologue. Below, we've rounded up the must-see moments from last night's /b … Humans may live longer and longer, but eventually we all grow old and die. This leads to a simple question: Is there an intrinsic maximum limit to human lifespan or not?

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. BI, Sales & CRM: Applications here include voice analytics to extract information from calls, automated customer response solutions, business data analytics, and sales targeting. To name a few, Palo Alto-based Mariana raised 2M in seed money from investors including Blumberg Capital; London-based True AI, previously seed funded by Entrepreneur First, entered the Microsoft Ventures Accelerator in Q3'16; another UK-based startup, Ripjar, raised funds from Winton Ventures in Q2'16. Three startups in the private sector using AI in e-commerce raised funding rounds this year: Reflektion raised 18M in Q1'16 from investors including Intel Capital, Battery Ventures, and Marc Benioff; ViSenze raised 10.5M in Series B from investors including Rakuten Ventures, Enspire Capital, and Phillip Private Equity; India-based Staqu raised angel funds in Q2'16.

Perspica added a new root cause analysis feature to its software-as-a-service (SaaS) visibility platform. The company says its root cause analysis detects issues within layers of the application stack and recommends potential fixes. Powered by machine learning, Perspica's root cause analysis feature is able to understand what is supposed to be happening in an application's performance, based on times of the day and days of the week, says JF Huard founder and CTO of Perspica. It is then able to analyze these metrics, detect anomalies, and determine the root cause. "Not only are we eliminating false positives, but we are also able to catch relevant problems by comparing an application's topology from day-to-day," Huard says.

Since PipelineDB released the developer preview of its realtime analytics API, Stride, one is tempted to ask whether APIs in connection with machine learning may replace the growing role of human data scientists in the data analytics space. To further explore this concept, ProgrammableWeb caught up with PipelineDB's President and Co-Founder, Jeff Ferguson. Ferguson and his team believe that machine learning can easily replace the role humans play in a number of data analytics tasks, and this shift has already begun to take place. "Machines can learn all sorts of things from streaming and siloed datasets, all with orders of magnitude more accuracy, speed, and scale than human beings," Ferguson told ProgrammableWeb. "A simple example is realtime a/b testing in advertising, where self-healing software systems can create, test, and iterate on multiple versions of ad copy, continuously weighting campaign spend toward ads with the highest click-through rates."