What is machine learning / ai? How to learn machine learning in practice? Neural Networks (often referred to as deep learning) are particular interesting. But there are a few questions. To answer these questions and give beginners a guide to really understand them, I created this interesting course.

Deep reinforcement learning (deep RL) is a popular and successful family of methods for teaching computers tasks ranging from playing Go and Atari games to controlling industrial robots. But it is difficult to use a single neural network and conventional RL techniques to learn many different skills at once. Existing approaches usually treat the tasks independently or attempt to transfer knowledge between a pair of tasks, but this prevents full exploration of the underlying relationships between different tasks. When humans learn new skills, we take advantage of our existing skills and build new capabilities by composing and combining simpler ones. For instance, learning multi-digit multiplication relies on knowledge of single-digit multiplication, while knowing how to properly prepare individual ingredients facilitates cooking dishes with complex recipes.

The tour lists 20 interesting real-world machine learning problems for data science enthusiasts to learn by solving.

Linear Regression, GLMs and GAMs with R demonstrates how to use R to extend the basic assumptions and constraints of linear regression to specify, model, and interpret the results of generalized linear (GLMs) and generalized additive (GAMs) models. The course demonstrates the estimation of GLMs and GAMs by working through a series of practical examples from the book Generalized Additive Models: An Introduction with R by Simon N. Wood (Chapman & Hall/CRC Texts in Statistical Science, 2006). Linear statistical models have a univariate response modeled as a linear function of predictor variables and a zero mean random error term. The assumption of linearity is a critical (and limiting) characteristic. Generalized linear models (GLMs) relax this assumption of linearity.

Google announces scholarship program to train 1.3 lakh Indian developers in emerging technologies 43569 views Want to be a millionaire before you turn 25? Study artificial intelligence or machine learning 42897 views

First, it was the AlphaGo AI from Google's DeepMind subsidiary which beat the world's best Go players at their own game to make a record. Then, an AI named Libratus, developed by the Carnegie Mellon University, outclassed Poker pros in a tournament to turn the world's attention towards the rapid pace at which AI is progressing. In the latest such example of an AI outsmarting human beings, a deep neural network model developed by Alibaba fared better than humans in a reading comprehension test. The AI model developed by Alibaba's Institute of Data Science and Technologies blazed past the SQuAD (Stanford Question Answering Dataset) test- one of the most reliable reading comprehension test for evaluating a machine's language skills- in a contest which pitted it against human rivals. Alibaba's AI scored a cumulative 82.44 Exact Match (EM) points, outscoring its human competitors who manged to put up 82.304 points on the scoreboard.

The classification of complex data usually requires the composition of processing steps. Here, a major challenge is the selection of optimal algorithms for preprocessing and classification (including parameterizations). Nowadays, parts of the optimization process are automized but expert knowledge and manual work are still required. We present three steps to face this process and ease the optimization. Namely, we take a theoretical view on classical classifiers, provide an approach to interpret the classifier together with the preprocessing, and integrate both into one framework which enables a semiautomatic optimization of the processing chain and which interfaces numerous algorithms.

A general Data Science workflow in machine learning consists of the following steps: gather data, clean and prepare data, train models and choose the best model based on validation and test errors or other performance criteria. Usually we – particularly we Data Scientists or Statisticians who live for numbers, like small errors and high accuracy – tend to stop at this point. Let's say we found a model that predicted 99% of our test cases correctly. In and of itself, that is a very good performance and we tend to happily present this model to colleagues, team leaders, decision makers or whoever else might be interested in our great model. We assume that our model is trustworthy, because we have seen it perform well, but we don't know why it performed well.

This guide to meant to help web developers, software engineers and other IT industry people to transition into analytics / data science industry. Last week, I was taking a guest lecture with one of the well known institutes in India. Rather (un)surprisingly, more than 60% of the students comprised of experienced IT Professionals. Most of them are facing a common problem, "I have been in IT / software / web development for more than a few years and want to up-skill myself in analytics. I have taken a few MOOCs and have tried using a few books / platforms. Still, I don't get it what should I do next?"

Nigel Toon, chief executive, said the UK's expertise in artificial intelligence should help. Other entrepreneurs argue that the government should do more to help. Last year, ministers outlined plans for a new "office of AI" and said the government would invest £45m to fund post graduate degrees in the field …