Learn to make the most from your data!
About This Video
- A practical guide to working with Machine Learning Techniques using R.
- Covers the latest techniques and code examples of R that you can perform using ML.
- This course offers a deep dive into techniques of ML using R to make your data more robust and easier to maintain.
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
Table of Contents
- Chapter 1 : Machine Learning Techniques in R
- Chapter 2 : Our First Dataset
- Chapter 3 : Regression
- Chapter 4 : Running a Random Forest Algorithm
- Chapter 5 : Naive Bayes Algorithm
- Chapter 6 : Data Visualization in R
- Chapter 7 : Now You Try a New Dataset
- Title: Getting Started with Machine Learning in R
- Release date: June 2018
- Publisher(s): Packt Publishing
- ISBN: 9781789139655