The E-learning course starts by refreshing the basic concepts of the analytics process model: data preprocessing, analytics and post processing. We then discuss decision trees and ensemble methods (bagging, boosting, random forests), neural networks, support vector machines (SVMs), Bayesian networks, survival analysis, social networks, monitoring and backtesting analytical models. Throughout the course, we extensively refer to our industry and research experience. The E-learning course consists of more than 20 hours of movies, each 5 minutes on average. Quizzes are included to facilitate the understanding of the material.

The true scale of the hack that hit the NHS could only become clear on Monday morning. Despite cyber security experts working hard to save hospitals from the attack, it may turn out to be far worse than previously thought after the weekend. In the NHS, experts are concerned that many pieces of equipment – not only computers but things like heart monitors – will be switched on for the first time after the weekend and may start being infected and spreading the malware all over again. More than 200,000 victims in around 150 countries have been infected by the ransomware which originated in the UK and Spain on Friday before spreading globally. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

Recently, I began a series on exploratory data analysis; so far, I have written about computing descriptive statistics and creating box plots in R for a univariate data set with missing values. Today, I will continue this series by analyzing the same data set with kernel density estimation, a useful non-parametric technique for visualizing the underlying distribution of a continuous variable.

The Madrid ASDM summer school is in its twelfth edition this year, with hundreds of students from all over the world having attended so far. It comprises 12 intensive (15 lecture hours) week-long courses, and a student may attend from one up to six courses. The courses cover topics such as Neural Networks and Deep Learning, Bayesian Networks, Big Data with Apache Spark, Bayesian Inference, Text Mining and Time Series, and each has theoretical as well as practical classes, done with R or python. While the summer school is mainly attended by people from academia - PhD students and researchers, people from the industry also assist. The students come from diverse backgrounds, ranging from biology to economics to mathematics and physics.

Right now, Machine Learning and Data Science are two hot topics, the subject of many courses being offered at universities today. Above, you can watch a playlist of 18 lectures from a course called Learning From Data: A Machine Learning Course, taught by Caltech's Feynman Prize-winning professor Yaser Abu-Mostafa. This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data.

About this course: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform. Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python).

Deep learning is the next step to machine learning with a more advanced implementation. Currently, it's not established as an industry standard, but is heading in that direction and brings a strong promise of being a game changer when dealing with raw unstructured data. Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language processing. Developers can avail the benefits of building AI programs that, instead of using hand coded rules, learn from examples how to solve complicated tasks. With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results.

Sure, there are lots of tutorials and overviews on gaining the insight you need into picking up machine learning, but many (most?) of them take the long view: get a foundation first, learn the basics next, then learn a bit of complementary theory before getting too far ahead of yourself in practical terms, take a step back, try your hand at a few examples, undertake a project on your own... This is all great advice, and a great approach to learning... well, almost anything. But let's say you're not starting from scratch. Or you don't have the patience to go through all of the motions. Let's say you want to hit the ground running and scramble under pressure to learn everything right now.

Artificial Intelligence (AI) pioneer Nvidia has announced it will train 100,000 developers in "deep learning" to bolster health care research and improve treatment in diseases like cancer. Deep learning is Nvidia's term for machine learning, the idea of pushing computers to learn the way a human would in order to progress what many are calling the next revolution in technology – machines that "think" like humans. Over the past decade, it's given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. In the past, cancer research institutes have looked into using Nvidia's latest advances in AI and deep learning to help pathologists with their overwhelming tasks. One project, Led by Andrew Beck, associate professor of pathology and director of bioinformatics at BIDMC, used an Nvidia Tesla K80 GPU supercomputer chip to speed up the process of training their computational models in breast cancer diagnosis.

While feats of Deep Learning has been gathering much attention, there were also breakthroughs in a related technology of Recurrent Neural Networks (RNN). RNNs hold great promise for learning general sequences, and have applications for text analysis, handwriting recognition and even machine translation. RNN is learning to paint house numbers (Andrej Karpathy) See a fantastic post by Andrej Karpathy, "The Unreasonable Effectiveness of Recurrent Neural Networks" where he uses RNNs to do amazing stuff like paint house numbers in this image, or generate text in the style of Paul Graham, Shakespeare, and even Latex. See below an excellent tutorial "General Sequence Learning using Recurrent Neural Networks" by Alec Radford, Indico Head of Research, who led a workshop on general sequence learning using recurrent neural networks at Next.ML in San Francisco, Feb 2015. Alec introduces RNNs and sketches how to implement them and cover the tricks necessary to make them work well.