This document provides an introduction to unsupervised learning, including defining unsupervised learning and how it differs from supervised learning. It discusses common unsupervised learning techniques like clustering, dimensionality reduction, K-means clustering, hierarchical clustering, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE). The document explores applications of unsupervised learning and its importance in extracting patterns from unlabeled data to gain hidden insights.

1. INTRODUCTION TO UNSUPERVISED LEARNING
SUBTITLE: EXPLORING PATTERNS WITHOUT LABELS
Mohammad Munaf Shaikh
MCS.23.17

2. OVERVIEW
• Definition of Unsupervised Learning
• Distinction from Supervised Learning
• Importance and Applications

3. BASIC CONCEPTS
• What is Unsupervised Learning?
• How does it differ from Supervised Learning?
• Examples: Clustering, Dimensionality
Reduction

4. CLUSTERING
• Definition of Clustering
• Types of Clustering Algorithms
• Examples: K-Means, Hierarchical Clustering,
DBSCAN

5. K-MEANS CLUSTERING
• Explanation of K-Means Algorithm
• Steps Involved
• Pros and Cons

6. HIERARCHICAL CLUSTERING
• Explanation of Hierarchical Clustering
• Types: Agglomerative, Divisive
• Dendrogram Representation

7. Dimensionality Reduction
• Definition and Need for Dimensionality
Reduction
• Techniques: PCA (Principal Component
Analysis), t-SNE (t-distributed Stochastic
Neighbor Embedding)
• Applications and Advantages

8. PRINCIPAL COMPONENT ANALYSIS
(PCA)
• Explanation of PCA
• Steps Involved
• Visualization of Principal Components

9. T-SNE (T-DISTRIBUTED
STOCHASTIC NEIGHBOR
EMBEDDING)
• Explanation of t-SNE
• Comparison with PCA
• Use Cases and Limitations

10. CONCLUSION
• In conclusion, unsupervised learning is a valuable approach
for extracting meaningful patterns and structures from
unlabeled data. Its significance lies in its ability to uncover
hidden insights and relationships within large datasets
without the need for explicit guidance. As technology
advances, unsupervised learning techniques are poised to
become even more sophisticated, enabling more nuanced
analyses and applications across various domains.
Continued research and development in this field hold the
promise of uncovering novel algorithms and
methodologies, further expanding the capabilities and
impact of unsupervised learning in data-driven decision-
making. Thank you for exploring the fundamentals of
unsupervised learning with us.