It covers Supervised & Unsupervised Machine Learning methods. The following topics are covered:
| Sl No | Topic |
| 1 | Introduction to Machine Learning (ML) |
| 2 | Linear and Multiple Regression |
| 3 | Classification & Logistic Regression |
| 4 | Ridge Regression & Lasso Regression |
| 5 | Naive Bayes Classifier |
| 6 | Time Series (Forecasting) |
| 7 | Decision Tree (Rule – Based) |
| 8 | Random Forest |
| 9 | K-Nearest Neighbour (Distance Based Learning) |
| 10 | Support Vector Machine (Distance Based Learning) |
| 11 | Ensemble Methods (Bagging and Boosting) |
| 12 | Performance Evaluation |
| 13 | Improving Performance |
| 14 | Unsupervised Learning |
| 15 | Clustering |
| 16 | K-means Clustering |
| 17 | Hierarchical Clustering |
| 18 | Principal Component Analysis (PCA) |
| 19 | Project |
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