About
**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**