Introduction to Machine Learning

What Is Machine Learning?

Machine Learning (ML) is a branch of Artificial Intelligence (AI) where computers learn patterns from data and improve their performance without being explicitly programmed for every rule.

Instead of saying:

“If this happens, do that.”

We say:

“Here is a lot of data — figure out the pattern yourself.”

Student-Friendly Definition

Machine Learning is when a computer learns from examples instead of just following fixed rules.

Just like students:

Study examples
Practice
Learn from mistakes
Get better over time

Machine learning models do the same thing with data.

How Machine Learning Works (4-Step Process)

Collect Data
Examples: photos, test scores, sensor data, text, clicks, etc.
Train a Model
The computer looks for patterns in the data.
Test the Model
We check how accurate its predictions are on data it has not seen before.
Make Predictions
The trained model is now used on new, unseen data.

Main Types of Machine Learning

1. Supervised Learning

In supervised learning, the computer is trained using labeled data. That means each example has an input and a correct output (the answer key).

Example:

Input: Student test answers
Output: Correct grade

Common uses:

Spam vs. not-spam detection in email
Face recognition in photos
Predicting housing prices

2. Unsupervised Learning

In unsupervised learning, the computer finds patterns without being told the correct answers. The data is not labeled.

Example:

Grouping customers by what they buy
Finding hidden trends in large datasets

Common uses:

Clustering similar items
Data exploration
Market and customer analysis

3. Reinforcement Learning

In reinforcement learning, the computer learns by trial and error and receives rewards or penalties based on its actions.

Example:

Video game AI learning how to win
Self-driving cars learning to stay in the lane
Robots learning to navigate a room

What Is a Neural Network?

A neural network is a machine learning model inspired by the human brain. It is made up of layers of connected “neurons.”

Input layer – receives the data
Hidden layers – process and transform the data
Output layer – produces the prediction or result

Neural networks are used in:

Facial recognition
Speech recognition
Chatbots (like ChatGPT)
Image generation and filters

Real-World Examples of Machine Learning

Area	Example
Phones	Face ID and fingerprint unlock
Streaming	Netflix and YouTube recommendations
Cars	Self-driving features and lane assist
School	AI tutoring and auto-grading tools
Security	Credit card fraud detection
Medicine	Cancer detection from medical images

Limitations of Machine Learning

Important: Machine learning is powerful but not perfect.

Needs large amounts of data to perform well.
Can be biased if the training data is biased.
Can make confident mistakes.
Does not truly think or understand like a human.

Machine Learning vs. Traditional Programming

Traditional Programming	Machine Learning
Human programmers write all the rules.	The computer learns rules from data.
Same output every time given the same input.	Can improve over time as it sees more data.
Hard to adapt to new situations.	Can adapt to new patterns and changes in data.

Key Takeaway

One-sentence summary:

Machine Learning is when a computer learns from data to make predictions or decisions without being directly programmed for each rule.