English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 0h 38m | 318 MB
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
Table of Contents
01 Welcome to the Course
02 About the Author
03 Introduction to Machine Learning
04 Cluster Determination – Within Groups Sum of Squares
05 K-Means Clustering
06 Classification Trees
07 Interpretation of Classification Tree Output
08 Tree Pruning and Misclassification
09 Regression Trees
10 Random Forests
11 Max-Min Normalization
12 Use of neuralnet – Training a Neural Network in R
13 Model Validation
14 Wrap Up and Thank You
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