Machine Learning Course with R

Curriculum

Topics Covered

• 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