Introduction to Machine Learning

Machine Learning (ML) is a branch of Artificial Intelligence that enables computers to learn patterns from data and make decisions without being explicitly programmed. Instead of manually writing rules, we provide examples, and the machine learns from them.

Why Machine Learning Matters

Machine Learning is widely used in modern applications:

• Image classification
• Speech recognition
• Recommendation systems
• Fraud detection
• Autonomous vehicles
• Natural language processing

ML helps organizations analyze data, automate tasks, and build intelligent services.

How Machine Learning Works

A typical ML workflow includes:

1. Collecting Data
2. Cleaning and Preprocessing
3. Choosing a Model
4. Training
5. Evaluating
6. Deploying

Here is a simple example of training a linear regression model in Python:

from sklearn.linear_model import LinearRegression
import numpy as np

# Example training data
X = np.array([[1], [2], [3], [4]])  # Features
y = np.array([2, 4, 6, 8])          # Target

model = LinearRegression()
model.fit(X, y)

prediction = model.predict([[5]])
print("Prediction for x=5:", prediction[0])