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.
Who Should Attend
- Beginner level courses for aspiring Data Scientist.
- Anyone who is interested in data science.
Course Curriculum
Course Modules
Exam & Certification
This course is not associated with any Certification.
