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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)