How Machines Learn

Now that you know what machine learning is, let's peek under the hood. How does a computer actually "learn" from data?

The answer involves three key concepts: features, labels, and optimization.

Features and Labels

In machine learning, we work with structured data:

• Features (X): The input variables used to make predictions
• Labels (y): The output we want to predict (in supervised learning)

Think of it like a test: features are the questions, labels are the answers.

The Learning Process: Finding Patterns

When a machine "learns," it's really doing mathematical optimization. Here's the intuition:

1. Start with a guess - The model makes random predictions
2. Measure the error - How wrong are the predictions?
3. Adjust and improve - Change the model to reduce errors
4. Repeat - Keep adjusting until errors are minimized

This process is called training.

What the Model Actually Learns

A model learns parameters - the numbers that define the pattern.

For a simple linear model predicting house prices:

price = weight₁ × size + weight₂ × bedrooms + bias

The model learns the optimal values for weight₁, weight₂, and bias.

The Cost Function: Measuring Errors

How does the model know if it's improving? It uses a cost function (also called loss function) to measure errors.

Common cost functions:

• Mean Squared Error (MSE): Average of squared differences
• Mean Absolute Error (MAE): Average of absolute differences

Lower cost = better model.

Gradient Descent: The Learning Algorithm

Most ML algorithms use gradient descent to minimize the cost function. Think of it like finding the lowest point in a valley while blindfolded:

1. Feel which direction goes down (calculate the gradient)
2. Take a step in that direction
3. Repeat until you can't go any lower

Generalization: The Ultimate Goal

The goal of ML isn't to memorize training data - it's to generalize to new, unseen data.

A good model:

• Learns the underlying patterns (not just memorizes examples)
• Performs well on data it has never seen before
• Balances complexity with simplicity

Key Takeaways

• Features are inputs, labels are outputs
• Learning = finding parameters that minimize prediction errors
• Cost functions measure how wrong the model is
• Gradient descent adjusts parameters to reduce errors
• The goal is generalization - performing well on unseen data

Next, we'll explore the real-world applications of machine learning!