Types of Machine Learning

There are 3 main types of Machine Learning. Let’s keep it simple and understand them with examples.

1. Supervised Learning

You teach the model using labeled data. That means the data already contains the answers.

📌 Example:
A student learns from a textbook that has questions and answers.

🧪 Real-world Examples:

• Predicting house prices
• Email spam detection

# Example: Predict marks based on study hours
from sklearn.linear_model import LinearRegression
X = [[2], [4], [6]]
y = [50, 70, 90]  # marks
model = LinearRegression()
model.fit(X, y)
print(model.predict([[5]]))  # Predict marks for 5 hours of study

2. Unsupervised Learning

The data has no labels. The model tries to find hidden patterns on its own.

📌 Example: A student reads a book in a language they don’t know, but tries to group similar words together.

🧪 Real-world Examples:

• Customer segmentation
• Grouping similar images

# Example: Group similar data points
from sklearn.cluster import KMeans
import numpy as np
X = np.array([[1, 2], [1, 4], [10, 2], [10, 4]])
kmeans = KMeans(n_clusters=2).fit(X)
print(kmeans.labels_)

3. Reinforcement Learning

Here, the model learns by trial and error. It gets rewards or penalties for the actions it takes.

📌 Example: Training a dog to sit by giving it a treat if it does it correctly.

🧪 Real-world Examples:

• Self-driving cars
• Game AI (like AlphaGo)