What is Machine Learning?

Imagine you want to build a program that can recognize spam emails. With traditional programming, you'd need to write explicit rules:

IF email contains "free money" THEN spam
IF sender not in contacts AND has attachments THEN spam
IF subject is all caps THEN spam
...and hundreds more rules

This approach has problems. Spammers adapt. New patterns emerge. You're always playing catch-up.

Machine learning takes a different approach: instead of writing rules, you show the computer examples and let it figure out the patterns.

Traditional Programming vs Machine Learning

Traditional Programming	Machine Learning
You write the rules	The computer learns the rules
Input: Data + Rules	Input: Data + Expected Output
Output: Answers	Output: The Rules (a model)

A Simple Definition

Machine Learning is a type of artificial intelligence where computers learn from data to make decisions or predictions, without being explicitly programmed with rules.

The key insight: instead of programming computers with instructions, we program them with data.

The Three Ingredients of Machine Learning

Every machine learning system needs:

1. Data

The examples the computer learns from. More quality data usually means better predictions.

2. Algorithm

The method used to find patterns in the data. Different algorithms work better for different problems.

3. Model

The result of training. A model is what makes predictions on new, unseen data.

When to Use Machine Learning

Machine learning shines when:

• Patterns exist in the data but are hard to define explicitly
• You have enough data to learn from (usually hundreds to millions of examples)
• The rules are complex or constantly changing
• Human expertise is limited or too slow

Good Use Cases

• Spam detection (patterns change constantly)
• Image recognition (too many visual patterns to program manually)
• Recommendation systems (complex user preferences)
• Fraud detection (subtle patterns across many transactions)

Not Ideal For

• Simple calculations (just write the formula)
• Problems with clear, unchanging rules
• Situations with very little data
• When you need 100% explainable decisions

The Machine Learning Workflow

Every ML project follows a similar pattern:

1. Collect Data - Gather examples relevant to your problem
2. Prepare Data - Clean and format it for the algorithm
3. Choose an Algorithm - Select the right tool for the job
4. Train the Model - Let the algorithm learn from the data
5. Evaluate - Test how well the model performs
6. Deploy - Put the model to work on real problems
7. Monitor - Watch performance and retrain as needed

Key Takeaways

• Machine learning lets computers learn from data instead of following explicit rules
• It requires data, an algorithm, and produces a model
• ML is best for problems with complex patterns and sufficient data
• Every ML project follows a workflow: data → training → evaluation → deployment

Next, we'll explore how machines actually learn from data!