For the latest installment of our Data Science 101 series, we have Bjorn Johansson, Executive Director Operations at Ericsson, presenting several initiatives where Ericsson is applying new methods to accelerate business transformation work.
The next installment of insideBIGDATA’s Data Science 101 series comes from our friends over at LinkedIn.
The Random forests machine learning algorithm is a popular ensemble method used by many data scientists to achieve good predictive performance in the classification regime. Fully understanding the nuances of this statistical learning technique is paramount to getting the most out of this algorithm – unfortunately, this means math. The presentation below is from machine learning course CPSC 540 at The University of British Columbia,
Brought to you by our friends over at the Stanford Center for Professional Development is this compelling data science education resource: “Expressing yourself in R” – by Hadley Wickham, Rice University.
Provided by our friends over at Edureka, Module 1 of their Apache Cassandra course below discusses the fundamental concepts of using a highly-scalable, column-oriented database to implement appropriate use cases.
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.