Introduction to Machine Learning

Machine Learning (ML) is a branch of Artificial Intelligence that enables computers to learn patterns from data and make decisions or predictions without being explicitly programmed for every rule.

Instead of writing thousands of conditions manually, we allow algorithms to analyze historical data, identify patterns, and improve their performance over time.

Why Machine Learning is Important

Modern applications generate massive amounts of data every second. Human-written rules cannot scale to handle this volume and complexity. Machine Learning solves this problem efficiently.

• Handles large-scale data automatically
• Finds hidden patterns humans may miss
• Improves accuracy over time
• Powers intelligent decision-making systems

Real-World Examples of Machine Learning

You interact with Machine Learning every day, often without realizing it.

• Email Spam Detection: Classifies emails as spam or not spam
• Netflix / YouTube: Recommends videos based on viewing history
• Google Search: Ranks pages based on relevance
• Banking: Detects fraudulent transactions
• Healthcare: Assists in disease prediction

How Machine Learning Works (High-Level View)

At a high level, Machine Learning follows a simple logic:

• Provide historical data
• Train an algorithm on that data
• Evaluate its performance
• Use the trained model to predict new outcomes

This process allows the model to generalize from known examples to unseen data.

Machine Learning vs Traditional Programming

Understanding this difference is critical.

• Traditional Programming: Rules + Data → Output
• Machine Learning: Data + Output → Rules (Model)

In ML, the computer learns the rules automatically instead of humans defining them.

Simple Machine Learning Example

Below is a very small Python example that shows the idea of ML prediction.

from sklearn.linear_model import LinearRegression

# Training data
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]

# Create and train model
model = LinearRegression()
model.fit(X, y)

# Predict
print(model.predict([[5]]))

Here, the model learns the pattern automatically instead of us writing a formula.

Key Terminology You Should Know

• Dataset: Collection of data used for learning
• Features: Input variables
• Label: Output variable
• Model: Learned representation of data
• Prediction: Output generated by the model

Mini Practice

Think about the following scenario:

A company wants to predict house prices based on size and location.

• What would be the features?
• What would be the label?
• Is this a prediction or classification problem?

Quick Quiz

In the next lesson, we will explore the different types of Machine Learning and understand where each type is used in real-world systems.