by Phil Rennert | Released June 2018 | ISBN: 9781789139655

Learn to make the most from your data!About This VideoA 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.In DetailDo 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 techniquesYou 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 Show and hide more

1. Chapter 1 : Machine Learning Techniques in R
   • The Course Overview 00:02:21
   • Applications of Machine Learning 00:03:59
   • Exploring Steps in End-To-End Processing of a Dataset 00:04:43
   • Goals of Machine Learning 00:06:57
   • Supervised Machine Learning 00:13:13
   • Unsupervised Machine Learning 00:11:40
   • Ensemble Methods 00:04:25
2. Chapter 2 : Our First Dataset
   • Preparing Our First Dataset 00:02:52
   • Cleaning Our Dataset 00:09:58
3. Chapter 3 : Regression
   • Linear and Logistic Regression 00:02:09
   • Regression on the Pima Dataset 00:04:22
4. Chapter 4 : Running a Random Forest Algorithm
   • Random Forest on the Pima Dataset 00:04:19
   • Running Random Forest 00:02:20
5. Chapter 5 : Naive Bayes Algorithm
   • Naive Bayes on the Pima dataset 00:01:46
   • Running Naive Bayes 00:04:21
   • Combining Algorithms 00:02:04
6. Chapter 6 : Data Visualization in R
   • Presenting Graphical Information 00:01:46
   • The ggplot2 Package 00:02:56
   • Plot Examples, Good and Bad 00:06:52
7. Chapter 7 : Now You Try a New Dataset
   • Dataset Part One 00:02:22
   • Dataset Part Two 00:06:04
   • Dataset Part Three 00:04:10
   • Dataset Part Four 00:02:25
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