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Introduction to Machine Learning.
- Supervised ML
- Unsupervised ML
- Semi-supervised ML
- Reinforcement Learning.
- Applications of Machine Learning.
2. Prerequisites for ML algorithms
- Regression and Classification idea with real life examples.
- What is a model and how does a model work ?
- How to define the Loss function for any problem?
- How do we assess the performance of a model ?
3. Basic Algorithms :
- LinearRegression
- LogisticRegression
- Naive Bayes
4. Evaluation Metrics for Classification:
- ConfusionMatrix
- Accuracy
- Precision
- Recall
- F1score
5. Evaluation Metrics for Regression:
- MSE
- RMSE
- MAE
6. Hands-on-Machine Learning with python
- Project: HeartDisease
Classification using Logistic Regression.
- CalculatingMetrics
- Inference from the model
7. Clustering Algorithms (Unsupervised)
- HierarchicalClustering
- KMeans,
8. Tree Based Algorithms:
- Decision Trees
- Random Forests
9. Q/A