In this blog post, I'll help you get started using Apache Spark's spark.ml Classification is a family of supervised machine learning algorithms that identify which category an item belongs to (for example, whether a cancer tissue observation is malignant or not), based on labeled examples of known items (for example, observations known to be malignant or not). Classification takes a set of data with known labels and pre-determined features and learns how to label new records based on that information. Features are the "if questions" that you ask. The label is the answer to those questions.

Last week, my Microsoft colleagues Bharath Sankaranarayan and Carl Saroufim presented a live webinar showing how you can predict a patient's length of stay at a hospital using SQL Server R Services. The recorded webinar is available for on-demand viewing now. The webinar is based on the Machine Learning Solution Template Predicting Length of Stay in Hospitals, which we covered here on the blog back in March. The solution is based on an instance of the Data Science Virtual Machine, which makes it easy to try it yourself. Just click the "Deploy" button to create your own instance in Azure with all of the data and scripts preloaded.

Document classification is a fundamental machine learning task. It is used for all kinds of applications, like filtering spam, routing support request to the right support rep, language detection, genre classification, sentiment analysis, and many more. To demonstrate text classification with scikit-learn, we're going to build a simple spam filter. While the filters in production for services like Gmail are vastly more sophisticated, the model we'll have by the end of this tutorial is effective, and surprisingly accurate. Spam filtering is kind of like the "Hello world" of document classification. However, something to be aware of is that you aren't limited to two classes.

Today Hank explores artificial intelligence, including weak AI and strong AI, and the various ways that thinkers have tried to define strong AI including the Turing Test, and John Searle's response to the Turing Test, the Chinese Room. Hank also tries to figure out one of the more personally daunting questions yet: is his brother John a robot? Get your own Crash Course Philosophy mug from DFTBA: http://store.dftba.com/products/crash... The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list... -- All other images and video either public domain or via VideoBlocks, or Wikimedia Commons, licensed under Creative Commons BY 4.0: https://creativecommons.org/licenses/... -- Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Crash Course Philosophy is sponsored by Squarespace.

There is much debate among scholars and practitioners about what data science is, and what it isn't. Does it deal only with big data? Is data science really that new? How is it different from statistics and analytics? One way to consider data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics.

Training strategies have long since stopped being considered as'nice to have' motivational activity and more and more organisations are expecting close alignment of training and business in order to make training strategies effective. Some of the key expectations of the business from training include outcome driven approach, velocity in training delivery, adaptation to the dynamic needs of the business and tuning to the millennial mindsets in the design of the programme. In this context, it is prudent to take advantage of digital capabilities and design the strategy such that role-specific competency road map is built, which in turn is matched with the training modules that the employees are supported with. The HR Information Systems, Performance Management system and the Learning Management Systems should be integrated and provide the bedrock system for talent development for the organisation. The learning paths put in place for the employees should be supported with the right learning ecosystem both offline and online and be able to switch from one world to the other in a seamless fashion.

I've been working on a tutorial series for creating self-driving cars in Grand Theft Auto 5 for a bit now. The most recent creation is the result of day or so worth of collecting training data, and about 4 days of actual training of the model. It's currently a 30-layer convolutional neural network, it works purely on a frame-by-frame basis with no preprocessing other than an image resize and grayscale. It makes actions based on the current frame's pixel data, with no memory of what it's been doing. I plan to eventually incorporate some form of memory with something like recurrent layers, but...baby steps at a time!

It's a few years old, but still acts as a great technical introduction to the topic.

How To Create a Website in a Weekend! (Project-Centered Course) State University of New York via Coursera (3 ratings) 8th May, 2017

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN).