Machine Learning
A subset of AI where systems learn patterns from data and improve their performance over time without being explicitly programmed for every scenario. ML algorithms build mathematical models from training data to make predictions or decisions.
Why It Matters
ML powers recommendation engines, fraud detection, medical diagnosis, and countless business applications. It turns raw data into actionable intelligence.
Example
Netflix recommending shows based on your viewing history, or a bank flagging unusual credit card transactions as potential fraud.
Think of it like...
Like learning to cook by tasting hundreds of dishes rather than just reading a recipe book — the system learns from experience, not instructions.
Related Terms
Supervised Learning
A type of machine learning where the model is trained on labeled data — input-output pairs where the correct answer is provided. The model learns to map inputs to outputs and can then predict outputs for new, unseen inputs.
Unsupervised Learning
A type of machine learning where the model learns patterns from unlabeled data without being told what the correct output should be. The algorithm discovers hidden structures, groupings, or patterns in the data on its own.
Reinforcement Learning
A type of machine learning where an agent learns to make decisions by taking actions in an environment and receiving rewards or penalties. The agent aims to maximize cumulative reward over time through trial and error.
Deep Learning
A specialized subset of machine learning that uses artificial neural networks with multiple layers (hence 'deep') to learn complex patterns in data. Deep learning excels at tasks like image recognition, speech processing, and natural language understanding.