Session 2
1. Unsupervised Learning
- Explanation of unsupervised learning
- Overview of clustering algorithms:
- K-means clustering
- Hierarchical clustering
- Overview of dimensionality reduction techniques:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Demonstration of implementing unsupervised learning algorithms
2. Model Evaluation
- Techniques for evaluating machine learning models
- Overview of performance metrics:
- Accuracy
- Precision, Recall, F1-score
- ROC curve
- Cross-validation and model selection
3. Hands-on Exercise
- Participants work on coding exercises and small projects to evaluate and fine-tune machine learning models using the techniques covered in the session
Instructor
Muniba Talha is a data scientist, educator and Python enthusiast.
The webinar will not be recorded. Language and materials are in English.