In this tutorial, we dive into unsupervised learning, a machine learning approach focused on discovering patterns and structures in unlabeled data. Explore its core concepts and applications like clustering, dimensionality reduction, and anomaly detection through practical examples.
Topics Covered:
1. Unsupervised Learning Basics
Understand how models find patterns and relationships in datasets without labeled outputs.
2. Clustering
Learn about grouping similar data points, demonstrated with K-means clustering in Python.
3. Dimensionality Reduction
Discover how to reduce dataset features while retaining essential information using PCA.
4. Anomaly Detection
Explore identifying outliers or abnormalities with techniques like Isolation Forest.
Certification:
Complete the unsupervised learning tutorial and earn your certification in Machine Learning Essentials.
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