Welcome to Module 2 — Machine Learning Fundamentals.

In this lesson, you will learn how machines can learn patterns from data and use those patterns to make predictions, classifications, and intelligent decisions. This module introduces the core concepts, workflows, and techniques that form the foundation of modern Machine Learning.

Topics Covered:

1. What is Machine Learning?
   Understand Machine Learning and how it enables computers to learn from data instead of relying entirely on explicitly programmed rules.
2. How Machines Learn from Data
   Learn how data is provided to a machine learning system and how algorithms identify patterns and relationships within that data.
3. Types of Machine Learning
   Understand the major approaches to Machine Learning:
   • Supervised Learning
   • Unsupervised Learning
   • Semi-Supervised Learning
   • Self-Supervised Learning
   • Reinforcement Learning
4. Supervised Learning
   Learn how models use labeled data to perform prediction and classification tasks.
5. Unsupervised Learning
   Understand how models discover hidden patterns, structures, and groups within unlabeled data.
6. Reinforcement Learning
   Learn how an agent learns through actions, rewards, and feedback to improve its decision-making.
7. Features and Labels
   Understand the role of input features and target labels when preparing data for Machine Learning.
8. Training and Testing Data
   Learn why datasets are divided into training and testing sets and how this helps evaluate model performance.
9. Regression
   Understand how Machine Learning models predict continuous numerical values such as prices, sales, demand, or temperatures.
10. Classification
    Learn how models categorize data into different classes, such as spam vs non-spam or positive vs negative.
11. Clustering
    Understand how clustering algorithms group similar data points without predefined labels.
12. Overfitting and Underfitting
    Learn why models can perform poorly when they either memorize training data or fail to capture important patterns.
13. Model Evaluation
    Understand the importance of evaluating Machine Learning models using appropriate performance measures.
14. Machine Learning Workflow
    Explore the complete workflow:
    Problem Definition → Data Collection → Data Preparation → Feature Engineering → Model Training → Evaluation → Deployment → Monitoring.
15. Real-World Applications
    Explore Machine Learning applications in finance, healthcare, e-commerce, marketing, recommendation systems, fraud detection, forecasting, and automation.

Mini Project:
Build a Machine Learning prediction or classification model using a real-world dataset. Prepare the data, train the model, evaluate its performance, and use it to generate predictions.

Learning Outcome:
By the end of this lesson, you will understand how Machine Learning systems learn from data, the major types of Machine Learning, common learning tasks, model training and evaluation, and the complete Machine Learning workflow.

Lesson Resources:
Video Lesson + Presentation PDF + Quize