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

Prerequisites

There are no prerequisites required to attend this course.

Download Syllabus

Course Modules