Part 2: R for Statistics, Modeling and Machine Learning--Summary - Video Tutorials & Practice Problems

<v ->I hope you enjoyed your learning experience.</v> We covered a broad range of topics and a great deal of material. We started with simple means, and worked up to modeling, including regression and decision trees. We learned the special methods needed for analyzing time series, notably arima models. Next, we covered a few different types of clustering, particularly k means. We then turned to some popular methods for building recommendation engines, text mining, and network analysis. After that, we learned how to use Caret for fine tuning machine learning models. Then, we fit a few Bayesian Models using Stan. With all that, you should be in a good position to use R for statistics and modeling.