Getting Started with Machine Learning in R

Course details

Do you want to turn your data to predict outcomes that make real impact and have better insights?

R provides a cutting-edge power you need to work with machine learning techniques

You will learn to apply machine learning techniques in the popular statistical language R. This course will get you started with Machine Learning and R by understanding Machine Learning and installing R. The course will then take you through some different types of ML. You will work with a classic dataset using Machine Learning. You will learn Linear and Logistic Regression algorithms and analyze the dataset. The course will take you through algorithms like Random Forest and Naive Bayes for working on your data in R. You will then see some of the excellent graphical tools in R, and some discussion of the goals and techniques for presenting graphical data. Analysis of the data set is demonstrated from end to end, with example R code you can use. Then youll have a chance to do it yourself on another data set.

By the end of the course you will learn how to gain insights from complex data and how to choose the correct algorithm for your specific needs.

Phil Rennert is a Principal Research Engineer in Information Science, in the overall business of extracting wisdom from information overload. He has a long track record of solving challenging technical problems, innovating new techniques where existing ones don't apply. He is extensively skilled in machine learning, natural language processing, and data mining.