Machine Learning with R
Beginnercourse
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.
4.6 (138 Reviews)
Language
- English
Topic
- Machine Learning
Enrollment Count
- 4.08K
Skills You Will Learn
- Machine Learning
Offered By
- CognitiveClass
Estimated Effort
- 3 Hours
Platform
- SkillsNetwork
Last Update
- April 20, 2024
About This course
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, 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
Language
- English
Topic
- Machine Learning
Enrollment Count
- 4.08K
Skills You Will Learn
- Machine Learning
Offered By
- CognitiveClass
Estimated Effort
- 3 Hours
Platform
- SkillsNetwork
Last Update
- April 20, 2024