This post is part of the Learning Machine Learning series. It's based on Chapter 1 and 2 of Python Machine Learning. Machine learning can be intimidating for a newcomer. The concept of a machine learning things alone is quite abstract. How does that work in practice?

A future in which human workers are replaced by machines is about to become a reality at an insurance firm in Japan, where more than 30 employees are being laid off and replaced with an artificial intelligence system that can calculate payouts to policyholders. Fukoku Mutual Life Insurance believes it will increase productivity by 30% and see a return on its investment in less than two years. The firm said it would save about 140m yen (£1m) a year after the 200m yen (£1.4m) AI system is installed this month. Maintaining it will cost about 15m yen (£100k) a year. The move is unlikely to be welcomed, however, by 34 employees who will be made redundant by the end of March.

Machine Learning is all about creating an artificial brain to perform a task by itself. In most cases that task is Prediction. To do these predictions, there are many technical options available. One popular question would be, whether to use Python or R. But before heading there, it is more important to understand some of the fundamental concepts to get started.

Nvidia Founder, President and CEO Jen-Hsun Huang introduces the Nvidia Spot, a USD 49.95 microphone and speaker that will let owners use Google Assistant anywhere in a home, as he delivers a keynote address at CES 2017 (Photo: Ethan Miller/Getty Images) LAS VEGAS--Nvidia is best known for the high-end computer graphics cards prized by hardcore gamers. If co-founder and CEO Jen-Hsun Huang delivers on his bold vision, more people are likely to recognize Nvidia as the powerhouse behind artificial intelligence in your home and in your vehicle. Clad in his trademark black leather jacket, Huang delivered a high energy opening night keynote address Wednesday night at CES, assuming a prestigious speaking slot that for years was reserved for Microsoft's Bill Gates and later his successor Steve Ballmer. Nvidia (NVDA) is already a star on Wall Street. It is coming off a two-year hot streak, with a particularly sizzling 224% gain in 2016 that made it the top performing stock in the S&P 500.

Sifting through lots of documents can be difficult and time consuming. Without an abstract or summary, it can take minutes just to figure out what the heck someone is talking about in a paper or report. And, if you need to get through hundreds of documents – good luck. Summarizer is an algorithm that extracts sentences from a text document, determines which are most important, and returns them in a readable and structured way. Automatic text summarization is part of the field of natural language processing, which is how computers can analyze, understand, and derive meaning from human language.

Honda's NeuV (New Electric Utility Vehicle) is a combination of ideas from the Japanese automaker, ranging from autonomous driving, to car sharing, electric charging, and connectivity. But perhaps the most ambitious idea put forth by Toyota in its latest concept car goes by the name of Hana (Honda Automated Network Assistant). It is meant to be a helper as well as a companion. "Artificial intelligence is becoming more and more important in vehicles because we want to kind of have a relationship with the vehicle rather than the vehicle just doing things on its own," says Nick Renner, who helped design NeuV. Hana can sense a driver's emotions through heart rate monitoring and face recognition, as well as carry on a conversation.

Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017, by Matthew Mayo Data Science Trends To Look Out For In 2017, by Andrew Dipper 50 Data Science, Machine Learning Cheat Sheets, updated, by Thuy T. Pham Data Science, Predictive Analytics Main Developments in 2016 and Key Trends for 2017 Why Deep Learning is Radically Different From Machine Learning 4 Cognitive Bias Key Points Data Scientists Need to Know 4 Reasons Your Machine Learning Model is Wrong (and How to Fix It) Big Data: Main Developments in 2016 and Key Trends in 2017 The 5 Basic Types of Data Science Interview Questions

In this article we will discuss the machine learning method called "decision trees", moving quickly over the usual "how decision trees work" and spending time on "why decision trees work." We will write from a computational learning theory perspective, and hope this helps make both decision trees and computational learning theory more comprehensible. The goal of this article is to set up terminology so we can state in one or two sentences why decision trees tend to work well in practice. Newcomers to data science are often disappointed to learn that the job of the data scientist isn't tweaking and inventing new machine learning algorithms. In the "big data" world supervised learning has been a solved problem since at least 1951 (see [FixHodges1951] for neighborhood density methods, see [GordonOlshen1978] for k-nearest neighbor and decision tree methods).

The year of (insert trend here) sounds familiar to anyone involved in the tech world. A new innovation arrives, it dominates the news cycle, and its popularity is followed by a chorus of talking heads declaring it as the product of the year. Reality is quite different: Tech trends don't really follow a yearly cycle and new computing paradigms usually take a lot more than a year to become indispensable parts of the industry. Take cloud computing for example: We said 2011 was going to be the year the cloud went completely mainstream. Now in 2017 we can see that, while definitely a fundamental piece of the IT puzzle, cloud computing still hasn't taken over and it's not likely to ever do so.

TYSONS, Va.--(BUSINESS WIRE)--CSC (NYSE: CSC) today announced the expansion of its strategic alliance with Microsoft through the advancement of Industrial Machine Learning (IML) solutions that enable data scientists to produce new data-derived enterprise insights, leading to better and more timely business decisions. Most large, data-rich companies fail to benefit from information on an enterprise-wide scale because they are not equipped to effectively capture and analyze available data sources. A recent survey from PwC and Iron Mountain found that 57 percent of businesses are unable to extract significant value from their information, and 23 percent are unable to derive any real value at all. Regardless of size, geography or sector, businesses are struggling to fully realize value from their information. "CSC has been working with Microsoft on industry-specific approaches to the problem of creating data science that works," said Dan Hushon, chief technology officer, CSC.