About the Course
This course dives into the basic of machine learning using R, an approachable and well-known programming language. You will learn about supervised and unsupervised learning, analyze the relation between statistical modelling and machine learning through comparisons.
You will also look into real-life examples to look at the various ways that machine learning affects the society. You will also explore many algorithms and models –
- Popular algorithms: Classification, Regression, Clustering, Dimensional Reduction
- Popular models: Train/Test Split, Root Mean Squared Error, Random Forests
Course Syllabus
- Machine Learning Languages, Types, and Examples
- Machine Learning vs Statistical Modelling
- Supervised vs Unsupervised Learning
- Supervised Learning Classification
- Unsupervised Learning
- K-Nearest Neighbors
- Decision Trees
- Random Forests
- Reliability of Random Forests
- Advantages & Disadvantages of Decision Trees
- Regression Algorithms
- Model Evaluation
- Model Evaluation: Overfitting & Underfitting
- Understanding Different Evaluation Models
- 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
- Dimensionality Reduction: Feature Extraction & Selection
- Collaborative Filtering & Its Challenges
Requirements
Recommended Existing Skills
- JupyterLab. You can enrol for our free course for JupyterLab here.
- Data Analysis with R. You can take the course here.
Course Staff
Saeed Aghabozorgi, PhD.
Saeed Aghabozorgi, PhD. He is a Sr. Data Scientist at IBM with a track record of developing enterprise-level applications that substantially increase clients’ ability to turn data into actionable knowledge. He is a data mining field researcher and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.
Other Contributors
Daniel Tran, Kevin Wong also contributed.
