Mastering Unsupervised Learning
At a Glance
"Mastering Unsupervised Learning" is a practical course designed to provide participants with a comprehensive understanding of unsupervised learning techniques. Participants will gain hands-on experience in applying clustering, dimensionality reduction, and anomaly detection algorithms to real-world datasets using Python.
Objectives
By the end of the course, participants will be equipped to:
- Understand the principles and applications of unsupervised learning in real-world scenarios
- Implement clustering algorithms such as k-means, hierarchical clustering, etc
- Apply dimensionality reduction techniques such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE)
- Detect anomalies and outliers in datasets using unsupervised learning techniques
- Evaluate and interpret the results of unsupervised learning algorithms
Prerequisite
Participants should have a basic understanding of Python programming and familiarity with fundamental concepts in machine learning. Prior exposure to supervised learning and basic statistics is recommended but not required.
Curriculum
- 8 Sections
- 0m Duration
Introduction to Unsupervised Learning
Clustering Algorithms
Dimensionality Reduction Techniques
Anomaly Detection
Preprocessing and Feature Scaling
Model Evaluation and Validation
Advanced Topics in Unsupervised Learning
Real-world Applications and Case Studies
Related Courses
Mastering Supervised Learning
- Supervised learning techniques
- Real-world case studies & implementation
0m
0
0
0
Python for Machine Learning
- Hands-on with ML algorithms
- Practical exercises with real-world datasets
0m
0
0
0