Introduction to Machine Learning.
- Supervised ML
- Unsupervised ML
- Semi-supervised ML
- Reinforcement Learning.
- Applications of Machine Learning.
2. Prerequisites for ML algorithms
3. Basic Algorithms :
4. Evaluation Metrics for Classification:
5. Evaluation Metrics for Regression:
- MSE
- RMSE
- MAE
6. Hands-on-Machine Learning with python
Classification using Logistic Regression.
- CalculatingMetrics
- Inference from the model
7. Clustering Algorithms (Unsupervised)
8. Tree Based Algorithms:
9. Q/A