Overview
Machine Learning – Unsupervised Learning
Unsupervised learning is a fascinating field of machine learning that discovers patterns, structures, and relationships within data without the need for labeled examples. This three-day course, designed for beginners, offers a comprehensive introduction to unsupervised learning techniques. Participants will gain a solid understanding of clustering, dimensionality reduction, and anomaly detection.
Skills Covered
By the end of this course, participants will:
- Understand the concepts of unsupervised learning and its significance in machine learning.
- Be proficient in various clustering techniques, including K-Means, Hierarchical, and DBSCAN.
- Gain practical experience in dimensionality reduction methods like Principal Component Analysis (PCA).
- Learn how to detect anomalies in data using unsupervised learning.
- Develop the skills to apply unsupervised learning to real-world data analysis and exploration.
- Feel confident to continue exploring more advanced machine learning techniques.
Prerequisites
There are no prerequisites required to attend this course.
Target Audience
- Beginner level courses for aspiring Data Scientist.
- Anyone who is interested in data science.
Module 1: Introduction to Unsupervised Learning
- What is unsupervised learning and its role in machine learning.
- Clustering vs. dimensionality reduction vs. anomaly detection.
- Data preprocessing and normalization for unsupervised learning.
- Clustering techniques: K-Means clustering.
- Evaluation metrics for clustering.
- Practical exercises and projects in K-Means clustering.
Module 2: Advanced Clustering and Dimensionality Reduction
- Hierarchical clustering.
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
- Dimensionality reduction and the curse of dimensionality.
- Principal Component Analysis (PCA).
- Non-negative Matrix Factorization (NMF).
- Hands-on projects in hierarchical clustering and dimensionality reduction.
Module 3: Anomaly Detection and Real-World Applications
- Anomaly detection: Definition and significance.
- Anomaly detection techniques.
- Applications of unsupervised learning in various domains.
- Real-world projects and applications.
- Model evaluation for unsupervised learning.
- Course wrap-up, Q&A, and next steps in the machine learning journey
Exam & Certification
This course is not associated with any Certification.
Training & Certification Guide
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