Part 2: R for Statistics, Modeling and Machine Learning — Introduction
Introdution
Part 2: R for Statistics, Modeling and Machine Learning — Introduction
Introdution - Video Tutorials & Practice Problems
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<v ->Hello, I'm Jared P. Lander.</v> R and data science is a huge part of my life. In addition to owning a boutique data science firm in New York City that specializes in R, I run the New York Open Statistical Programming Meetup, the world's largest R meetup, teach R programming at Columbia University as part of the Intro to Data Science course, and I'm the author of the bestselling book R for Everyone. In this series of videos, we learn how to use R for statistics, modeling, and machine learning. This LiveLessons video has a broad audience. It is meant both for people learning to program for the first time and serious codeslingers with knowledge of other languages. Expert Excel users looking to make their lives easier by automating tasks through scripting will greatly benefit from this series. And of course, statisticians old and new will gain a lot from learning R, whether it is their first language or they are transitioning from Sas or Stata. We start with the basics of statistics, such as averages, standard deviations, probability distributions, and t tests. This is all in preparation for the workhorse of statistics, regression. Linear models and their generalized extensions are covered in detail, with just a little theory to supplement all the computation. After this, we are ready for even more complex models such as the elastic net, Bayesian regression, and splines. Time series gets plenty of attention as we look at Arima, Var, and Garch models. Our foray into unsupervised machine learning covers K means, K medoids, and hierarchical clustering. We then turn our attention to recommenderlab for building a recommendation engine, R text tools for text mining, and IRLBA for fast matrix factorization. Continuing along that path, we analyze and visualize network data with I-Graph. Using Caret, we fine-tune the parameters for a few machine learning models. Lastly, we integrate Stan into R for fitting Bayesian models. I hope you will find these lessons informative and enjoyable as you walk through modeling in R.