English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 21m | 347 MB
Learn all the skills you need to start using R for machine learning
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.
Key Features
- A course that helps you to get started with programming and machine learning quickly
- Case studies to help you understand the concepts in more detail
- An all-in-one mini project on machine learning
- Embedded with assessments that will help you revise the concepts that you have learned in this course
The course is intended for both professionals and students. Specifically anyone with none or minimal prior experience with programming.
What You Will 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
Table of Contents
01 Course Overview
02 Setting up R and RStudio
03 R Packages
04 Useful Packages for Working with Data and Machine Learning
05 Data Frames
06 Indices and Subsetting
07 Loops
08 Functions
09 Case Study – Iris
10 Data Visualization using ggplot2
11 Case Study
12 Supervised Learning
13 Unsupervised Learning
14 Deep Learning and Real-World Examples
15 An Introduction to Reinforcement Learning
16 Machine Learning – Mini Project
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