About the course
Learn to make the most from your data!
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 you’ll 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.
All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Getting-Started-with-Machine-Learning-in-R
Style and Approach
A comprehensive course packed with step-by-step instructions, working examples, and helpful advice. This course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest
What You Will Learn
- Process a classic dataset, from data cleaning to presenting results with effective graphics.
- Explore different types of ML
- Clean your dataset and run a linear regression fit
- Use ML on your dataset by running a random forest algorithm
- Run naive Bayes algorithm on your dataset
- Present graphical information about your dataset
- Use the different packages of R to represent your data
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.
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