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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**

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Timing: 12:00 AM - 12:00 AM IST
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