Support Vector Machines (SVM) is an important and widely used machine learning algorithm. In order to fully understand SVMs, you need to have a fundamental understanding of how the statistical learning method functions. Here is a useful lecture on SVM coming from MIT OpenCourseware.
Our friends over at DataCamp just came out with a cool new infographic entitled “Become a Data Scientist in 8 easy steps.” This hits home to a lot of people who are trying to enter this new industry hoping to satisfy a lot of unfilled job openings. The question is how best to make this transition. The useful infographic below will help answer this question by outlining the process of becoming a data scientist.
The video presentation below is a highly compelling talk by Stanford University professor and Coursera co-founder, Dr. Andrew Ng. Andrew addresses a graduate summer school audience at UCLA’s IPAM (Institute for Pure & Applied Mathematics) on the topic – Deep Learning, Feature Learning.
From the SciPy2013 conference, here is a compelling talk “Data Agnosticism: Feature Engineering Without Domain Expertise” by Nicholas Kridler of Accretive Health in Chicago.
Revolution Analytics, the only commercial provider of open source R software, today announced two new offerings that support the open source R community and elevate R’s capabilities to enterprise-level performance. Revolution R Open is a free, open source R distribution that enhances R performance, makes it easier to share R scripts and improves collaboration on R-based advanced analytics applications.
Welcome back to our series of articles sponsored by Intel – “Ask a Data Scientist.” Once a week you’ll see reader submitted questions of varying levels of technical detail answered by a practicing data scientist – sometimes by me and other times by an Intel data scientist. This week’s question is from a reader who wants to know more about the “curse of dimensionality.”