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Clustering and Classification with Machine Learning in R
Master machine learning in R: Learn clustering, classification, and data preprocessing techniques for practical data science applications.
Course Cost
₹ 2,699
Beginner
Skill Level
12 Hours
Self-paced lessons
This comprehensive course teaches supervised and unsupervised machine learning using R, focusing on practical applications in data science. Students learn to implement various clustering algorithms, classification methods, and dimension reduction techniques. The course covers data preprocessing, feature selection, and model evaluation using popular R packages. Through hands-on exercises, learners gain practical experience with K-means clustering, Random Forests, PCA, and more.
English
What you'll learn
Master data preprocessing and wrangling in R Studio
Implement unsupervised clustering techniques like K-means
Apply supervised learning methods including Random Forests
Use dimensional reduction techniques and feature selection
Create and evaluate machine learning models
Perform practical data analysis using R packages
Skills you'll gain
This course includes:
426 Minutes PreRecorded video
11 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 10 modules in this course
This course provides a comprehensive introduction to machine learning techniques in R, covering both supervised and unsupervised learning methods. Students learn practical data science skills including data preprocessing, clustering algorithms, classification techniques, and dimension reduction. The curriculum includes hands-on implementation of various algorithms such as K-means clustering, Random Forests, PCA, and feature selection methods. Through practical exercises and real-world examples, learners develop proficiency in using R for machine learning applications.
Introduction to the Course
Module 1 · 36 Minutes to complete
Read in Data from Different Sources in R
Module 2 · 54 Minutes to complete
Data Pre-processing and Visualization
Module 3 · 1 Hours to complete
Machine Learning for Data Science
Module 4 · 26 Minutes to complete
Unsupervised Learning in R
Module 5 · 1 Hours to complete
Feature/Dimension Reduction
Module 6 · 41 Minutes to complete
Feature Selection to Select the Most Relevant Predictors
Module 7 · 53 Minutes to complete
Supervised Learning Theory
Module 8 · 30 Minutes to complete
Supervised Learning: Classification
Module 9 · 2 Hours to complete
Additional Lectures
Module 10 · 1 Hours to complete
Fee Structure
Payment options
Financial Aid
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
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Frequently asked Questions
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