Machine Learning
Machine Learning (ML) is a subset of AI where systems learn from data without being explicitly programmed. Instead of writing rules, you feed the system examples and it discovers patterns on its own.
Types of Machine Learning
Supervised Learning: The model trains on labeled data (input-output pairs). Used for classification and regression tasks like spam detection and price prediction.
Unsupervised Learning: The model finds hidden patterns in unlabeled data. Used for clustering, anomaly detection, and dimensionality reduction.
Reinforcement Learning: An agent learns by interacting with an environment and receiving rewards or penalties. Used in game playing, robotics, and autonomous driving.
Deep Learning
Deep Learning is a specialized subset of ML that uses artificial neural networks with many layers (hence "deep"). These networks can automatically learn hierarchical representations of data.
Deep learning excels at unstructured data — images, audio, text — where traditional ML struggles. It powers breakthroughs like image recognition, speech synthesis, and large language models.
Key Difference
Traditional ML requires manual feature engineering — a human expert decides what patterns to look for. Deep learning automatically discovers the relevant features from raw data, but requires much more data and compute power.