Introduction to Machine Learning

📘 Introduction to Machine Learning

Machine Learning is a method where we teach computers to recognize patterns in data. Instead of writing every single rule ourselves, we provide the machine with a “Data Diet” and let it figure out the rules for itself.

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📂 The Three Main Types of Machine Learning

To make it simple for students, we categorize ML based on how the “Teacher” (the human) helps the “Student” (the computer).

1. Supervised Learning (The Guided Student)

In this type, the computer is given a “Cheat Sheet” or labeled data.

• How it works: We show the computer an image and tell it, “This is an apple”.
• The Goal: The machine learns the relationship between the input (image) and the label (apple).
• Project Connection: This is exactly what we do on Day 2 with the Happy vs. Sad text classifier.

2. Unsupervised Learning (The Explorer)

Here, the computer is given data with no labels. It must find hidden patterns on its own.

• How it works: We give the computer a mix of different fruits and don’t tell it what they are.
• The Goal: The machine groups them by color or shape (e.g., “All round red things in Group A”) without knowing they are called “apples.”
• Daily Example: How YouTube suggests a new category of videos based on a “cluster” of things you’ve watched.

3. Reinforcement Learning (The Gamer)

This is learning through trial and error, similar to how you learn to play a video game.

• How it works: The machine takes an action and receives a “Reward” for a good move or a “Penalty” for a bad one.
• The Goal: To maximize the reward over time.
• Daily Example: A robot learning to walk or navigate a maze without hitting walls.

Presentation:

SC_AI2_Introduction to Machine Learning by Infinite Engineers