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Module 10
Unsupervised Learning and Dimensionality Reduction
1
Introduction to Clustering
Explain the goal of clustering and identify use cases for it
2
Understanding K-Means Clustering
Describe the k-means clustering algorithm step-by-step
3
K-Means Clustering: Implementation and Optimal K Selection
Implement k-means clustering using scikit-learn and select the optimal number of clusters
4
Clustering Performance Evaluation with Silhouette Score
Evaluate clustering performance using metrics like the silhouette score
5
Why Reduce Dimensions?
Explain the purpose of dimensionality reduction for visualization and model performance
6
PCA: Reducing Dimensions with Maximum Variance
Explain how Principal Component Analysis (PCA) reduces dimensionality by finding directions of maximum variance
7
Applying PCA with Scikit-learn
Apply PCA to a dataset using scikit-learn
8
Visualizing PCA-Clustering Results in 2D
Visualize the results of clustering on a 2D projection of data created with PCA
Previous module
Supervised Learning: Classification Models
Next module
Neural Network Fundamentals