In the world of big data, analysis by traditional statistical methods is no longer sufficient. The amount of data and the number of potential relationships that could be analyzed is simply too complex to conduct manually. In this video, you'll learn a better way: how to automate the analysis of big data by using machine learning techniques in R. You'll explore the cornerstone methods of machine learning (i.e., k-means clustering, decision trees, random forests, and neural networks); you'll incorporate these methods inside R to construct a set of machine learning algorithms; and then you'll deploy these algorithms against a real-world dataset to perform a high-value business analysis of the data. Course prerequisites include basic knowledge of linear algebra, probability, statistics, and familiarity with R.
- Gain hands-on experience with machine learning and R using a real-world dataset
- Understand k-means clustering, decision trees, random forests, and neural networks
- Learn how to run a variety of machine learning techniques using R
- Discover how to test the validity of results through use of training and test data
Michael Grogan is a data scientist who specializes in R, Python, and Shiny. As a consultant, Michael provides data science solutions to clients in healthcare, finance, and government. As an educator, Michael creates data science tutorials for organizations such as Data Science Central, Sitepoint, and O'Reilly Media. He holds a Master's degree in business economics from University College Cork.
Table of Contents
Overview of Machine Learning Methods
- Introduction to Machine Learning 00:03:05
- Cluster Determination: Within Groups Sum of Squares 00:02:51
- K-Means Clustering 00:04:07
- Classification Trees
- Regression Trees and Random Forests
- Neural Networks
- Wrap Up and Thank You 00:01:42
- Title: Machine Learning in R—Automated Algorithms for Business Analysis
- Release date: January 2018
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492028529