📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Basics of Supervised and Unsupervised Learning, from foundations to a certified capstone project.
Outline
What is Machine Learning? • Types of Machine Learning • Real-World Applications of ML
Outline
What is Supervised Learning? • Understanding Labeled Data • Introduction to Regression and Classification • Examples of Supervised Learning Applications
Outline
What is Unsupervised Learning? • Understanding Unlabeled Data • Introduction to Clustering and Pattern Discovery • Examples of Unsupervised Learning Applications
Outline
Basic Evaluation Concepts • Comparing Supervised vs Unsupervised Learning • Strengths and Limitations of Each Approach • Simple Performance Understanding
Outline
Real-World Use Cases in Business, Healthcare, and Technology • Choosing the Right Learning Approach • Introduction to Advanced Machine Learning Topics • Mini Learning Activity / Concept-Based Practice
Earn government-registered certification in Basics of Supervised and Unsupervised Learning
e-Certificate and e-Marksheet issued on successful completion.
