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

Supervised Learning: The algorithm learns from labeled training data. Examples include spam detection and image classification.

Unsupervised Learning: The algorithm finds patterns in unlabeled data. Clustering and dimensionality reduction are common applications.

Reinforcement Learning: The algorithm learns by interacting with an environment and receiving rewards or penalties.

Getting Started with Python

The most popular libraries for ML in Python are scikit-learn, TensorFlow, and PyTorch.

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Key Concepts

Features: The input variables used by the model to make predictions.

Labels: The output variable the model is trying to predict.

Training: The process of feeding data to the algorithm so it can learn patterns.

Inference: Using a trained model to make predictions on new data.

Last updated: March 2026