R and Machine Learning Fundamentals
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R and Machine Learning Fundamentals

Learn all the skills you need to start using R for machine learning
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
3 students enrolled
Created by Packt Publishing
Last updated 7/2017
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Current price: $10 Original price: $200 Discount: 95% off
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  • 1.5 hours on-demand video
  • 7 Articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Explore the basic data types and control structures in R
  • Understand the split-apply-combine paradigm for data manipulation
  • Learn how to visualize data using ggplot2
  • Get familiar with various classes of machine learning algorithms: supervised, unsupervised, reinforcement, and deep learning
  • Understand basics of the caret package and machine learning workflows by completing a mini project
View Curriculum
  • Specifically anyone with none or minimal prior experience with programming.

R is one of the most popular languages used for machine learning and arguably, the best entry point to the fascinating world of machine learning (ML). If you're interested to explore both the programming and machine learning world with R, then go for this course.

This course is a blend of text, videos, code examples, assessments, case studies, and a mini project which together makes your learning journey all the more exciting and truly rewarding. It is meticulously designed and developed in order to empower you with all the right and relevant information on R.

Let’s take a look at this learning journey. The course starts with teaching you how to set up the R environment, which includes installing RStudio and R packages. You will learn the various data types, operators, and control structures. You will then understand the split-apply-combine paradigm. You will see how to build effective data visualization using the widely popular ggplot2 library. The course also demonstrates a case study on the very famous Iris dataset.

Moving ahead, you will be introduced to the various aspects of machine learning—supervised, unsupervised, reinforcement, and deep learning. Machine learning aims to uncover hidden patterns, unknown correlations, and find useful information from data. This course aims to make you proficient enough to write R programs to perform various ML tasks irrespective of your previous programming experience and skill level. You will go through the different types of machine learning and when it's to be used along with a case study. Finally, you will look at a full-fledged project that will teach you how to build machine learning models.

By the end of this course, you will have a good knowledge of R principles in both programming and machine learning which you can use as a springboard to further develop your expertise.

About the Author

Akash Tandon is a Data Engineer at RedCarpet (a Y-Combinator and Google Startup Launchpad startup) with his primary responsibilities including setup and maintenance of the organization’s Machine Learning infrastructure. He’s also a data science competitions enthusiast and has worked on various competitions with notable results on various platforms, including Kaggle, HackerEarth and Analytics Vidhya. An avid open source software (OSS) enthusiast, he has worked thrice with the the organization, R project of Statistical computing, under the Google Summer of Code programs, both as a student and mentor.

Who is the target audience?
  • The course is intended for both professionals and students.
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Curriculum For This Course
23 Lectures
R – Getting Started
4 Lectures 11:10

This video gives an overview of the entire course.      

Preview 03:13

This video helps you install R and RStudio on your system. Here is what is in store for you:

  • Setting up R for Windows, Linux, and Mac OS X
  • Installing RStudio, the most popular IDE 
Setting up R and RStudio

Now that you have R and RStudio installed in your system, let’s explore the different packages that R provides. Here is what is in store for you:

  • An overview of R packages
  • Installing R packages 
R Packages

In this video, you will explore the packages that can be used for data and machine learning:

  • Learning some important R packages used for working with data and machine learning
  • Installing the packages 
Useful Packages for Working with Data and Machine Learning
Data Types and Subsetting
3 Lectures 20:45
Getting Started with Data Types

In this video, we will look at all the settings that need to be done in Unity before we start the process of building to VR:

  • Understand why data frames is widely used
  • Learn how to create your own data frames
  • Explore useful functions to work with data frames
Preview 05:03

In this video, you will learn how to work with indices and subsetting. Here's in what in store for you:

  • Understand what subsetting is
  • Learn how to perform subsetting on vectors, matrices, lists, and data frames  
Indices and Subsetting
Control Structures
3 Lectures 12:41

In this video, you will learn how to work with different loops in R. Particularly, you will:

  • Understand what loops are and how to use them
  • Learn to use nested loops
  • Explore loop functions (break, next)
Preview 07:39

In this video, you will learn how to write functions in R. Particularly, you will:

  • Understand what functions are
  • Learn how to write functions 
2 Lectures 10:50

In this video, you will take a look at a case study based on the famous Iris dataset. Particularly, you will learn:

  • Analyze the Iris dataset with the help of the split-apply-combine paradigm
  • Familiarize with the dplyr package 
Case Study – Iris
Visualizing Data in R
2 Lectures 07:27
An Introduction to ggplot2

In this video, you will understand the basics of the ggplot2 library and how to use it for data visualization. Here is what is in store for you:

  • Understanding the importance of data visualization
  • Basics of ggplot2
  • Building data visualization with ggplot2
Data Visualization using ggplot2
An Introduction to Machine Learning
2 Lectures 05:40
Getting Started with Machine Learning

The video demonstrates a case study on real-world applications of machine learning. Here is what is in store for you:

  • E-mail classification example
  • Self-driving cars example
Case Study
Supervised and Unsupervised Learning
2 Lectures 05:02

The video demonstrates what supervised learning is and where it can be applied. Here is what is in store for you:

  • Get an overview of supervised learning
  • Understand the classification technique
  • Learn the regression technique 
Preview 03:11

In this video, demonstrates what supervised learning is and where it can be applied. Particularly, you will:

  • Understand unsupervised learning
  • Learn about clustering 
Unsupervised Learning
The Caret Package
1 Lecture 04:01
ML in R – the Caret Package
Deep Learning
2 Lectures 06:48
Deep Learning – Neural Networks

The video demonstrates deep learning and its examples. Here is what is in store for you:

  • Get an overview of deep learning
  • Explore examples of deep learning 
Deep Learning and Real-World Examples
Reinforcement Learning
1 Lecture 04:48

The video demonstrates reinforcement learning and its examples. Particularly, you will:

  • Get an overview of reinforcement learning
  • Understand how is it different from supervised and unsupervised learning
  • Go through a real-world application of reinforcement learning 
An Introduction to Reinforcement Learning
1 More Section
About the Instructor
Packt Publishing
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Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

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