Tree based learning algorithms are considered to be one of the best and mostly used supervised learning methods. Tree based methods empower predictive models with high accuracy, stability and ease of interpretation. Unlike linear models, they map non-linear relationships quite well. They are adaptable at solving any kind of problem at hand (classification or regression). Methods like decision trees, random forest, gradient boosting are being popularly used in all kinds of data science problems. Hence, for every analyst (fresher also), it's important to learn these algorithms and use them for modeling. This tutorial is meant to help beginners learn tree based modeling from scratch. After the successful completion of this tutorial, one is expected to become proficient at using tree based algorithms and build predictive models. Note: This tutorial requires no prior knowledge of machine learning.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

DUBLIN--(BUSINESS WIRE)--Research and Markets has announced the addition of the "Artificial Intelligence (Ai) Market By Technology (Machine Learning, Natural Language Processing (Nlp), Image Processing, And Speech Recognition), Application & Geography - Global Forecast To 2020" report to their offering. The author forecasts the artificial intelligence market to grow from USD 419.7 Million in 2014 to USD 5.05 Billion by 2020, at a CAGR of 53.65% from 2015 to 2020. The major factors driving the growth of this market include diversified application areas of AI, improved productivity, and increased level of customer satisfaction. In addition, the rising demand for intelligent systems is expected to propel the growth of the market in the next five years. The scope of this report covers the artificial intelligence market by technology, application, and region.

Chatbots are on the rise, but what are they and why is everyone talking about (and to) them? Facebook is widely expected to launch an app store for chatbots at its developer conference this week. Meanwhile, Microsoft has described chatbots as the "new apps" with chief executive Satya Nadella saying that they "unlock conversation as a platform". The BBC "created" its own one-off chatbot to answer some of the burning questions you may have about this latest technology. What can I help you with Jane?

NVIDIA today unveiled the NVIDIA DGX-1, the world's first deep learning supercomputer to meet the unlimited computing demands of artificial intelligence. The NVIDIA DGX-1 is the first system designed specifically for deep learning -- it comes fully integrated with hardware, deep learning software and development tools for quick, easy deployment. It is a turnkey system that contains a new generation of GPU accelerators, delivering the equivalent throughput of 250 x86 servers.1 The DGX-1 deep learning system enables researchers and data scientists to easily harness the power of GPU-accelerated computing to create a new class of intelligent machines that learn, see and perceive the world as humans do. It delivers unprecedented levels of computing power to drive next-generation AI applications, allowing researchers to dramatically reduce the time to train larger, more sophisticated deep neural networks. NVIDIA designed the DGX-1 for a new computing model to power the AI revolution that is sweeping across science, enterprises and increasingly all aspects of daily life.

To the computer scientist Leslie Valiant, "machine learning" is redundant. In his opinion, a toddler fumbling with a rubber ball and a deep-learning network classifying cat photos are both learning; calling the latter system a "machine" is a distinction without a difference. Valiant, a computer scientist at Harvard University, is hardly the only scientist to assume a fundamental equivalence between the capabilities of brains and computers. But he was one of the first to formalize what that relationship might look like in practice: In 1984, his "probably approximately correct" (PAC) model mathematically defined the conditions under which a mechanistic system could be said to "learn" information. Valiant won the A.M. Turing Award -- often called the Nobel Prize of computing -- for this contribution, which helped spawn the field of computational learning theory. In a 2013 book, also entitled "Probably Approximately Correct," Valiant generalized his PAC learning framework to encompass biological evolution as well.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Editor's note: this post is part of an ongoing series exploring developments in artificial intelligence. First, collect huge amounts of training data -- probably more than anyone thought sensible or even possible a decade ago. Second, massage and preprocess that data so the key relationships it contains are easily accessible (the jargon here is "feature engineering"). Finally, feed the result into ludicrously high-performance, parallelized implementations of pretty standard machine-learning methods like logistic regression, deep neural networks, and k-means clustering (don't worry if those names don't mean anything to you -- the point is that they're widely available in high-quality open source packages). Google pioneered this formula, applying it to ad placement, machine translation, spam filtering, YouTube recommendations, and even the self-driving car -- creating billions of dollars of value in the process.

Autonomous vehicles would have to be driven hundreds of billions of miles to create enough data to clearly demonstrate their safety, according to a new report. Under even the most-aggressive tests, it would take existing fleets of autonomous vehicles hundreds of years to log sufficient miles to adequately assess their safety compared to human-driven vehicles. Researchers say the findings suggest that in order to advance autonomous vehicles into daily use, alternative testing methods must be developed to supplement on-the-road testing. Autonomous vehicles (Google's car pictured) would have to be driven hundreds of billions of miles to create enough data to clearly demonstrate their safety, according to a new report. The report, Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability?, was written by Rand Corporation.

Probabilistic graphical models has been quite a roller coaster over the past few years. Social network analysis is growing less rapidly as well. While the results herein may not come as much surprise, it's fascinating to see the extent to which AI, machine learning, and especially deep learning has proliferated and grown among advanced course offerings. It is incredibly exciting to witness the opportunities for predictive insights & machine intelligence that are affecting every industry and business application, and reassuring that knowledge and understanding of these core areas is only going to increase -- and perhaps become core tools & techniques for an upcoming wave of engineers.