Module 2

Basics of Machine Learning
Ever wondered how Netflix knows exactly what show you'll love next, or how your email filters out spam with incredible accuracy? Welcome to Module 2 of "Basics of Machine Learning," where you'll discover the fascinating technology behind these everyday miracles.
At its core, Machine Learning is like teaching a child - but instead of learning from experience, computers learn from data. Just as a child learns to recognize cats after seeing many examples, ML systems analyze patterns in data to make intelligent decisions. From self-driving cars to medical diagnosis, this technology is revolutionizing how we solve complex problems across every industry.
In this module, you'll dive into the core principles that make Machine Learning possible. We'll explore different learning approaches - from supervised learning that helps predict house prices, to reinforcement learning that powers game-playing AI. 
By the end of this module, you'll understand not just how ML works, but also how to identify opportunities where it can solve real-world challenges in your own field.
Learning Objectives
Lesson 2.1: What is Machine Learning?

Gain a clear understanding of what Machine Learning entails, including its definition, principles, and applications in various domains.

Lesson 2.2: The Role of Data in Machine Learning

Learn about the crucial role of data in Machine Learning, including data preprocessing, feature engineering, and the impact of data quality on model performance.

Lesson 2.3: Introduction to Python and its Libraries for ML

Familiarize yourself with the Python programming language, its libraries, and tools commonly used in Machine Learning applications.