Training on Unsupervised Learning: Clustering & Dimensionality Reduction

Module 1: Introduction to Unsupervised Learning

• Overview of supervised vs. unsupervised learning

• Applications in identifying patterns in unlabeled data

• Types of unsupervised tasks: clustering, association, reduction

• Introduction to Python tools for unsupervised ML (e.g., scikit-learn, seaborn)

Module 2: Clustering Fundamentals

• Concept and use cases for clustering in analytics

• Distance metrics: Euclidean, Manhattan, Cosine

• K-means clustering and centroid-based methods

• Customer segmentation using machine learning case study

Module 3: Advanced Clustering Techniques

• Hierarchical clustering and dendrogram analysis

• DBSCAN and density-based clustering

• Gaussian Mixture Models and soft clustering

• Discovering hidden groups in datasets through real-world examples

Module 4: Evaluating Clustering Performance

• Internal metrics: Silhouette score, Davies-Bouldin index

• External metrics: ARI, NMI when ground truth is available

• Cluster validation and choosing the right number of clusters

• Business application: Clustering algorithms for market analysis

Module 5: Dimensionality Reduction Concepts

• Curse of dimensionality in high-dimensional data

• Feature selection vs. dimensionality reduction

• Importance of data visualization in high-dimensional spaces

• Identifying noise and redundancy in datasets

Module 6: Principal Component Analysis (PCA)

• Mathematical foundation of PCA

• Applying PCA for visualization and feature reduction

• Explaining variance and interpreting components

• Use case: Reducing data complexity with ML

Module 7: Non-Linear Dimensionality Reduction Techniques

• t-SNE for visualization and cluster separation

• UMAP for preserving global structure

• Comparison between PCA, t-SNE, and UMAP

• Best practices for using non-linear reduction tools

Module 8: Feature Engineering & Data Transformation

• Scaling and normalization of features

• Encoding categorical data for clustering

• Dealing with missing values and outliers

• Creating interpretable features for reduction and segmentation

Module 9: Integrating Clustering & Reduction for Strategy

• Combining PCA and clustering for robust segmentation

• Customer segmentation using machine learning dashboard

• Use case: Public health, education, or economic segmentation

• Interpretation for strategic planning and decision-making

Module 10: Capstone Project and Visualization

• Real-world project: Segment customers or markets

• Create and present a clustering report with reduced features

• Use of visualization libraries (Plotly, Matplotlib, Seaborn)

• Project review and roadmap for applying insights at work