Introduction to Machine Learning

Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed.

Types of Machine Learning

1. Supervised Learning

Supervised learning uses labeled data to train models. Common algorithms include:

• Linear Regression
• Decision Trees
• Support Vector Machines
• Neural Networks

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

2. Unsupervised Learning

Unsupervised learning finds patterns in unlabeled data:

• K-Means Clustering
• PCA (Principal Component Analysis)
• Autoencoders

3. Reinforcement Learning

Agents learn by interacting with an environment and receiving rewards or penalties.

Key Concepts

• Features: Input variables used for prediction
• Labels: Target variables in supervised learning
• Training/Testing Split: Dividing data for model evaluation
• Overfitting: When a model memorizes training data

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