Ted Dunning (Chief Application Architect at MapR) and Ellen Friedman have written a new O'Reilly Media book on "Practical Machine Learning – Innovations in Recommendation" (released in January 2014). This book examines one of the most interesting, fun, and powerful data science applications in the big data universe: recommendation systems. For me, this was one of the most interesting applications of data mining that immediately captured my imagination after I embarked on the journey to data science (drifting away from my astrophysics roots) about a dozen years ago. It is also one of the most common use cases that are taught in data science MOOCs and other analytics training courses. I believe that the love affair with recommender systems can be partly attributed to two things.

Lately, artificial intelligence has been very much the hot topic in Silicon Valley and the broader tech scene. To those of us involved in that scene it feels like an incredible momentum is building around the topic, with all kinds of companies building A.I. into the core of their business. There has also been a rise in A.I.-related university courses which is seeing a wave of extremely bright new talent rolling into the employment market. But this is not a simple case of confirmation bias - interest in the topic has been on the rise since mid-2014, as the graph below of Google Search frequency for the terms "Artificial Intelligence" and "Machine Learning" (more on that shortly) show: The noise around the subject is only going to increase, and for the layman it is all very confusing. Depending on what you read, it's easy to believe that we're headed for an apocalyptic Skynet-style obliteration at the hands of cold, calculating supercomputers, or that we're all going to live forever as purely digital entities in some kind of cloud-based artificial world.

Data Science at the Command Line is a new book written by Jeroen Janssens. This website contains information about the webcast from August 20th, instructions on how to install the Data Science Toolbox, and an overview of all the command-line tools discussed in the book. This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. Discover why the command line is an agile, scalable, and extensible technology.

This course will help you understand all of this and more. More than that, this course will enable you to create models for real-world predictive analytics problems like the ones you discussed above. At the end of the course, you will form teams and will compete against the best data scientists in the world in a Kaggle competition.

To learn more about this license, http://creativecommons.org/licenses/b... This lecture was recorded on April 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.

We've seen lots of interest in the community to visualize the output of an Azure ML model using Power BI. What's more, if one could operationalize Azure ML models through the Power BI service, that would be even more awesome. You could then have Power BI surface the latest output of your fraud model or Twitter sentiment about your product. We now have a tutorial that shows you how to do just that. The tutorial assumes your data is in an Azure SQL database but you can tailor the example to a data source of your choice, like a local CSV or an on-premises SQL installation.

Azure ML Hands-On Lab content is now available on GitHub and open for community contributions. With developers in mind, this content is aimed at providing general information about machine learning, and uses Azure ML as the toolset. You'll find basic concepts such as theoretical and practical definitions of ML and common use case scenarios, as well as hands-on lab materials with step-by-step instructions on how to activate an Azure ML subscription, how to install the most popular local ML solution development environments and an introduction to R and Python. Find out more about Azure ML Hands-On Lab content on the Cortana Intelligence and Machine Learning Blog.

Keras is a Python library for deep learning that wraps the efficient numerical libraries TensorFlow and Theano. Keras allows you to quickly and simply design and train neural network and deep learning models. In this post you will discover how to effectively use the Keras library in your machine learning project by working through a binary classification project step-by-step. Binary Classification Worked Example with the Keras Deep Learning Library Photo by Mattia Merlo, some rights reserved. The dataset we will use in this tutorial is the Sonar dataset.

After earning two advanced degrees, completing three postdocs, working in three countries, and finally reaching the stage when I am setting up my own lab, I realize that three lessons taught by three great mentors have influenced how I think about doing science. These lessons, each of which came at just the right time in my career, have helped me probe new intellectual territories and enjoy my work. Looking back, I appreciate the way that my mentors supported my development as a researcher and imparted valuable advice that still guides how I approach my work and career. Now, as I am moving into the role of adviser myself, I hope to be able to pass these lessons on to my current and future students. "Three great mentors have influenced how I think about doing science."

It's Python without having to install anything. Of course, the downside to these is your kid will still be staring at a computer screen.