Very interesting compilation published here, with a strong machine learning flavor (maybe machine learning book authors - usually academics - are more prone to making their books available for free). Many are O'Reilly books freely available. Here we display those most relevant to data science. I haven't checked all the sources, but they seem legit. If you find some issue, let us know in the comment section below.

There is no decision, except, Logistic Regression is parametric, while IDT is non-parametric. What you need to know is, they give you similar stuff you'll need, but using different approaches. AND, one is preferable over the other in certain situations. For eg, IDT can be very helpful when you want to know rules to create your segments! Also, when you have no clue what your data looks like, IDT is a good place to start.

Auto makers need to push regulators around the world for consistent rules to allow autonomous cars to proliferate, the chief executive of Renault SA RNO -1.16 % and Nissan Motor NSANY -1.97 % Co. said Wednesday. Carlos Ghosn, speaking at the New York International Auto Show, said he expects autonomous vehicles to become more commonplace in coming years, eventually changing lanes on highways and driving through cities on their own. His companies plan to offer 10 autonomous-drive models by 2020. But differing regulations could present hurdles to clearing them for operation, he said. "It's very important that we…lobby in every single country with the regulatory authorities to take our eyes off the road and our hands off the wheel," Mr. Ghosn said, noting that his companies are working with U.S. and Japanese regulators.

A practitioner/learner of machine learning (ML) 'potentially' comes from either a computer science (CS) background or more of a Science, Maths and Engineering background. Therefore, there generally tends to be a gap in knowledge, understanding and culture between these two camps. The CS minded people typically value structure, organization, efficacy and replication over speed and scientific achievement and vice versa. An ideal situation would be where this is balanced. Since my class doesn't offer me this luxury, I decided to take a crack at it on my own.

Researchers have developed a system to revolutionize future roadways, doing away with traffic lights to eliminate long waits and reduce emissions. The project, initiated by the MIT Senseable City Lab, proposes'slot-based intersections' for self-driving cars, which designate individual times for each vehicle to enter the roadway. To avoid traffic delays and short stops, autonomous vehicles would use sensors to communicate with those around them while maintaining a safe distance. Researchers have developed a system to revolutionize future roadways, doing away with traffic lights to eliminate long waits and reduce emissions. The project proposes'slot-based intersections' for self-driving cars, which designate individual times for each vehicle to enter the roadway Each autonomous car would be equipped with sensors to'communicate' with the cars around it.

What are we trying to predict? How are we measuring if we are getting it right? Which departments are a part of this Data Science project? Who is my internal customer? When does this have to be completed?

Google made its Cloud Machine Learning platform, which is used by Google Photos, Translate, and Inbox, available to developers today. During the announcement at NEXT 2016, Google's Cloud Platform conference, Alphabet Chairman Eric Schmidt called machine learning "what's next," according to TechCrunch. Machine learning powers features like the Google app's speech recognition and the smart reply feature in the Inbox app, and Google sees the technology as the future of computing. "Cloud Machine Learning will take machine learning mainstream, giving data scientists and developers a way to build a new class of intelligent applications," Fausto Ibarra, Google's director of product management wrote in a blog post. "It provides access to the same technologies that power Google Now, Google Photos, and voice recognition in Google Search as easy to use REST APIs."

I am sure a number of you have heard about machine learning. A dozen of you might even know what it is. And a couple of you might have worked with machine learning algorithms too. You see where this is going? Not a lot of people are familiar with the technology that will be absolutely essential 5 years from now.

There's been a huge amount of progress in machine learning in the last five years, largely due to breakthroughs in deep learning. You might not be directly aware of it, but we're at the beginning of a machine learning boom right now, a neural network renaissance. Google and Facebook are pouring huge amounts of money into deep learning. In the next few years, we're going to see the fruits of these investments. Self-driving cars, automatic closed captions and more accurate machine translation come to mind, but I would argue that the ramifications are going to quickly expand much beyond this.

Robotics companies and academics descended on a resort in Palm Springs this week for an invitation-only conference organized by Amazon.com Inc. to bring together experts in artificial intelligence, robotics, home automation and space exploration. At the event, held at the Parker Palm Springs, there were robotic arms dueling with light sabers from Star Wars, seminars about imbuing machines with human values and a celebrity appearance by film director Ron Howard, known for his portrayal of Richie Cunningham in the sitcom Happy Days. Jeff Bezos mingled with attendees; one said he drank single-malt whiskey with Amazon's chief executive officer. Representatives from companies such as Rethink Robotics, Toyota Motor Corp., and others attended the conference, called MARS, short for Machine-Learning (Home) Automation, Robotics and Space Exploration.