# Mastering Machine learning with R

Certificate Paid English Intermediate Free
Last updated on September 9, 2024 2:15 pm
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Explore the algorithms and models involved in mastering machine learning with R and discover the standard packages for Exploratory Data Analysis.

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Learn more about the approaches, applications and methods of machine learning in this free online course. We’ll show you examples of applications that use this method and measures tasks best solved using this science. In addition, explore how to use the Root-mean-square deviation method to assess the closeness between a model and the actual dataset. Expand your machine learning knowledge and skills by signing up today!

### What You Will Learn In This Free Course

• Outline the examples of applications…
• Define the concept of Machine learni…
• Explain the goal of a linear regress…
• Discuss the concept of overfitting a…
• Outline the examples of applications that use Machine learning
• Define the concept of Machine learning
• Explain the goal of a linear regression model
• Discuss the concept of overfitting a model
• Evaluate the standard packages used for Exploratory Data Analysis
• Describe data manipulation and visualization in R
• Recognize the most common Linear Regression cost function
• Identify data processing steps in R
• Recall the reasons why data preprocessing is necessary
• Machine Learning and Data Preprocessing

In this module on Machine Learning and Data Preprocessing. You will learn about the examples of applications that use Machine Learning and examples of tasks best solved using Machine Learning. You will also gain insight into the main focus of supervised learning.

Linear Regression

In this module on Linear Regression, you will learn that the goal of a model is not to uncover the truth but to discover a simple approximation that is still useful. You will also learn to use the Root Mean Squared Deviation method to assess the closeness between a model and the actual dataset.

Diploma in Mastering Machine Learning with R – First Assessment

This assessment enables you to review your learning so you can determine your knowledge and understanding of Modules 1 – 2. You must score 80% or more to pass this assessment.; Module

Exploratory Data Analysis

In this module on Exploratory Data Analysis, you will learn how Exploratory Data Analysis helps you develop an understanding of your data. You will also learn about conditions that have to be met for tabular data to be considered tidy and the standard packages used for Exploratory Data Analysis.

Linear and Logistic Regression

In this module on Linear and Logistic Regression, you will learn about the most common Linear Regression cost function and how to test the null hypothesis. You will also learn how to calculate the correlation between different variables within a dataset and compare the data frames in R.

Diploma in Mastering Machine Learning with R – Second Assessment

This assessment enables you to review your learning so you can determine your knowledge and understanding of Modules 3 – 4. You must score 80% or more to pass this assessment.; Module

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Mastering Machine learning with R
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