| Weeks | Topics |
| 1 |
Introduction and Basic Concepts
|
| 2 |
Data Preprocessing, Missing Data, Imbalanced Data Handling
|
| 3 |
Data Preprocessing, Outlier Detection, Feature Engineering Techniques
|
| 4 |
Model Selection, Model Evaluation Metrics
|
| 5 |
Supervised Learning Regression
|
| 6 |
Supervised Learning Classification
|
| 7 |
Midterm Week
|
| 8 |
Supervised Learning Decision Trees
|
| 9 |
Supervised Learning - Support Vector Machines (SVM)
|
| 10 |
Supervised Learning K-Nearest Neighbors (KNN)
|
| 11 |
Unsupervised Learning - Clustering
|
| 12 |
End-to-End Machine Learning Model Building, Model Improvements
|
| 13 |
Project Presentations
|
| 14 |
Project Presentations
|