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
|