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Machine Learning with R

New – Use R to apply common machine learning algorithms to real-world scenarios in this self-paced course.

GK# 7728

Course Overview


This course is your entry-point to machine learning using the highly popular R language. You will start by organizing your data and then predicting it with the help of various examples. The first example will demonstrate predicting the murder arrest rate based on arrest data for a given state. Explore R Studio and libraries, how to apply linear regression, how to score test sets, and plotting test results on a Cartesian plane. Then use logistic regression to forecast insurance types based on medical treatment. This example demonstrates labeling and scaling data, how cross-validation works, and how to apply Logistic regression. Finally, you will move on, where you will use the caret package in R to simplify some of the classification steps.


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What You'll Learn

  • Harness the power of R to build common machine learning algorithms with real-world data science applications
  • Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
  • Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
  • Classify your data with Bayesian and nearest neighbour methods
  • Predict values by using R to build decision trees, rules, and support vector machines
  • Forecast numeric values with linear regression, and model your data with neural networks


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On-Demand Outline

Get introduced to the world of Machine Learning and understand how R fits into the picture

Learn R data structures that you can use to manage and understand data

Understand the various regression and classification techniques

Learn to work with various algorithms to get the job done

Evaluate the performance of your models and tweak them to perfection

Who Should Attend


This course is for developers who wish to learn about the ML techniques and how you can implement them in R. Knowledge of R is assumed.

Course Delivery

This course is available in the following formats:


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