| Attribute | Detail |
|---|---|
| Format | Online, self-paced course |
| Level | Basic / Beginner |
| Duration | 2–3 Weeks |
| Certification | e-Certification |
| Fee | ₹199 / $20 |
| Tools | Machine Learning Supervised Learning Unsupervised Learning Regression Classification Clustering |
About the Basics of Supervised and Unsupervised Learning Course
The Basics of Supervised and Unsupervised Learning course is a free, beginner-friendly self-paced program designed to introduce learners to the two core types of machine learning. The course explains how models learn from labeled and unlabeled data, and how these approaches are used to solve real-world problems.
Learners will understand key concepts such as classification, regression, clustering, pattern discovery, and model evaluation. This course is ideal for beginners who want to build a strong foundation in machine learning before moving to advanced AI and data science topics.
Program Highlights
• Free beginner-level machine learning course
• Online self-paced learning format
• Simple explanation of supervised and unsupervised learning
• Covers classification, regression, and clustering basics
• Real-world examples and use cases
• Suitable for students and first-time learners
• e-Certification upon successful completion
Course Curriculum
Module 1: Introduction to Machine Learning
- What is Machine Learning?
- Types of Machine Learning
- Real-World Applications of ML
Module 2: Supervised Learning Basics
- What is Supervised Learning?
- Understanding Labeled Data
- Introduction to Regression and Classification
- Examples of Supervised Learning Applications
Module 3: Unsupervised Learning Basics
- What is Unsupervised Learning?
- Understanding Unlabeled Data
- Introduction to Clustering and Pattern Discovery
- Examples of Unsupervised Learning Applications
Module 4: Model Evaluation and Comparison
- Basic Evaluation Concepts
- Comparing Supervised vs Unsupervised Learning
- Strengths and Limitations of Each Approach
- Simple Performance Understanding
Module 5: Applications and Next Steps
- 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
Tools, Techniques, or Platforms Covered
Machine Learning Supervised Learning Unsupervised Learning Regression Classification Clustering
Real-World Applications
- Predicting outcomes using supervised learning models
- Classifying data in healthcare, finance, and business
- Discovering hidden patterns in large datasets
- Segmenting customers and analyzing behavior
- Preparing for advanced machine learning and data science learning
Who Should Attend & Prerequisites
- This course is suitable for students, beginners, freshers, and professionals who want to understand the core types of machine learning.
- It is also useful for learners from engineering, computer science, business, management, mathematics, statistics, and other data-related fields.
Certification
