Getting Started with Machine Learning in R

Getting Started with Machine Learning in R

Learn to make the most from your data!

Bestseller
Created By: Phil Rennert
16.05 9.62

About This Course

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

Other Information

  • Certificate will provided in this course on Completion
  • Full lifetime access
  • Available on Mobile & Laptop

What Students Will Learn In Your Course?

  • Process a classic dataset, from data cleaning to presenting results with effective graphics.
  • Explore different types of ML
  • Clean your dataset and run a linear regression fit
  • Use ML on your dataset by running a random forest algorithm
  • Run naive Bayes algorithm on your dataset
  • Present graphical information about your dataset
  • Use the different packages of R to represent your data

Are There Any Course Requirements Or Prerequisites?

This course is for aspiring data scientists who are familiar with the basics of the R language and data frames, and have a basic knowledge of statistics. You are not expected to have any knowledge of machine-learning systems.

Who Are Your Target Students?

If you are looking to understand how the R programming environment and packages can be used to develop machine learning systems, then this is the perfect course for you.

Course Content

  • 23 lectures
  • 01:47:53
  • The Course Overview
    00:02:20
  • Applications of Machine Learning
    00:03:59
  • Exploring Steps in End-To-End Processing of a Dataset
    00:04:42
  • Goals of Machine Learning
    00:06:56
  • Supervised Machine Learning
    00:13:12
  • Unsupervised Machine Learning
    00:11:40
  • Ensemble Methods
    00:04:25
  • Preparing Our First Dataset
    00:02:52
  • Cleaning Our Dataset
    00:09:57
  • Linear and Logistic Regression
    00:02:09
  • Regression on the Pima Dataset
    00:04:21
  • Random Forest on the Pima Dataset
    00:04:18
  • Running Random Forest
    00:02:20
  • Naive Bayes on the Pima dataset
    00:01:46
  • Running Naive Bayes
    00:04:21
  • Combining Algorithms
    00:02:04
  • Presenting Graphical Information
    00:01:46
  • The ggplot2 Package
    00:02:55
  • Plot Examples, Good and Bad
    00:06:51
  • Dataset Part One
    00:02:22
  • Dataset Part Two
    00:06:03
  • Dataset Part Three
    00:04:09
  • Dataset Part Four
    00:02:25
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Packt Publication

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  • 18 Students
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