Unsupervised Learning and Pattern Recognition

Basics of Machine Learning
Algorithms
Unsupervised Learning and Pattern
Recognition
 Identifying patterns or structure in data without
labeled responses
 Examples: clustering, dimensionality reduction,
anomaly detection
Key Factors Enabling Machine Learning in
Today's Era
 Increased data availability and storage capabilities
 Advances in algorithms and computational power
 Improved understanding of machine learning
principles
Applications of Machine Learning in Real
Life
 Image and speech recognition
 Natural language processing
 Recommender systems
 Predictive maintenance
Types of Machine Learning Paradigms
 Supervised Learning and Labeled Data
 Learning from example input-output pairs
 Regression and classification tasks
 Unsupervised Learning and Pattern Recognition
 Identifying patterns or structure in data
 Clustering and dimensionality reduction tasks
 Reinforcement Learning and Feedback Mechanisms