In-depth introduction to machine learning in 15 hours of expert videos

In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book. If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors’ website. Chapter 1: Introduction (slides, playlist) Chapter 2: Statistical Learning (slides, playlist) Interviews (playlist)

How To Get Started With Machine Learning Algorithms in R - Machine Learning Mastery R is the most popular platform for applied machine learning. When you want to get serious with applied machine learning you will find your way into R. It is very powerful because so many machine learning algorithms are provided. In this post you will discover how you can over come this difficulty with machine learning algorithms in R, with pre-prepared recipes that follow a consistent structure. Lots of Algorithms, Little Consistency The R ecosystem is enormous. A problem that I experienced when starting out with R was that the usage to each algorithm differs from package to package. All this means that if you want to try a few different algorithms from different packages, you must spend time figuring out how to fit and make predictions with each method in turn. I summarize these difficulties as follows: Inconsistent: Algorithm implementations vary in the way a model is fit to data and the way a model is used to generate predictions. Build an Algorithm Recipe Book Algorithm Recipes in R

Big Data Applications and Analytics MOOC - Course Geoffrey gives some amazing statistics for total storage; uploaded video and uploaded photos; the social media interactions every minute; aspects of the business big data tidal wave; monitors of aircraft engines; the science research data sizes from particle physics to astronomy and earth science; genes sequenced; and finally the long tail of science. The next slide emphasizes applications using algorithms on clouds. This leads to the rallying cry ''Use Clouds running Data Analytics Collaboratively processing Big Data to solve problems in X-Informatics educated in data science'' with a catalog of the many values of X ''Astronomy, Biology, Biomedicine, Business, Chemistry, Climate, Crisis, Earth Science, Energy, Environment, Finance, Health, Intelligence, Lifestyle, Marketing, Medicine, Pathology, Policy, Radar, Security, Sensor, Social, Sustainability, Wealth and Wellness''