At a Glance

Name: Machine Learning with R
Rating: 4.6 (103 reviews)

Learn what machine learning is all about in this beginner-friendly course. Through videos and labs, learn how to apply different machine learning techniques such as classification, clustering, neural networks, regression, and recommender systems.

About This Course

This Machine Learning with R course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.

Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!

Explore many algorithms and models:

Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.

Get ready to do more learning than your machine!

COURSE SYLLABUS

Module 1 - Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning

Machine Learning Languages, Types, and Examples
Machine Learning vs Statistical Modelling
Supervised vs Unsupervised Learning
Supervised Learning Classification
Unsupervised Learning

Module 2 - Supervised Learning I

K-Nearest Neighbors
Decision Trees
Random Forests
Reliability of Random Forests
Advantages & Disadvantages of Decision Trees

Module 3 - Supervised Learning II

Regression Algorithms
Model Evaluation
Model Evaluation: Overfitting & Underfitting
Understanding Different Evaluation Models

Module 4 - Unsupervised Learning

K-Means Clustering plus Advantages & Disadvantages
Hierarchical Clustering plus Advantages & Disadvantages
Measuring the Distances Between Clusters - Single Linkage Clustering
Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
Density-Based Clustering

Module 5 - Dimensionality Reduction & Collaborative Filtering

Dimensionality Reduction: Feature Extraction & Selection
Collaborative Filtering & Its Challenges

REQUIREMENTS

R programming

RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE

You have to do hands-on lab for this course. The tool that you use for hands-on is called Jupyter and it is one of the most popular tools used by data scientists. If you are not familiar with Jupyter, I would recommend that you take our free Data Science Hands-on with Open Source Tools.
This hands-on lab requires that you have working knowledge of R programming language as it applies to data analytics. If you don't feel you have sufficient skill in Data Analysis with R, I recommend you take Data Analysis with R courses.

Course Staff

Saeed Aghabozorgi

Saeed Aghabozorgi, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.

Cognitive Class Course Development Team

Thanks to the course contributors: Daniel Tran, Kevin Wong

Offered By: CognitiveClass

Machine Learning with R

At a Glance