by Michael Grogan | Released January 2018 | ISBN: 9781492028529

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 datasetUnderstand k-means clustering, decision trees, random forests, and neural networksLearn how to run a variety of machine learning techniques using RDiscover how to test the validity of results through use of training and test dataMichael 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. Show and hide more Publisher resources Download Example Code

1. Introduction
   • Welcome to the Course 00:01:46
   • About the Author 00:01:28
2. Overview of Machine Learning Methods
   • Introduction to Machine Learning 00:03:05
3. Clustering (K-Means)
   • Cluster Determination: Within Groups Sum of Squares 00:02:51
   • K-Means Clustering 00:04:07
4. Classification Trees
   • Classification Trees 00:04:24
   • Interpretation of Classification Tree Output 00:02:27
   • Tree Pruning and Misclassification 00:02:32
5. Regression Trees and Random Forests
   • Regression Trees 00:03:24
   • Random Forests 00:02:54
6. Neural Networks
   • Max-Min Normalization 00:02:27
   • Use of neuralnet: Training a Neural Network in R 00:02:48
   • Model Validation 00:02:49
7. Conclusion
   • Wrap Up and Thank You 00:01:42
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