Machine Learning

Machine learning (ML) is a branch of artificial intelligence where computer systems learn patterns from data and use those patterns to make predictions or decisions, rather than following explicitly programmed rules. It's the foundation that modern AI is built upon.

Core types of machine learning: supervised learning (learning from labeled examples — "this email is spam, this one isn't"), unsupervised learning (finding patterns in unlabeled data — clustering similar customers), reinforcement learning (learning through trial and error with rewards — training game-playing AI), and self-supervised learning (creating labels from data itself — how LLMs learn from text).

The machine learning workflow involves: data collection (gathering relevant training data), feature engineering (identifying relevant patterns in data), model training (the algorithm learns from data), evaluation (testing performance on held-out data), deployment (putting the model into production), and monitoring (tracking performance over time).

Machine learning powers countless applications: recommendation systems (Netflix, Spotify), fraud detection (banking, e-commerce), speech recognition (Siri, Alexa), image recognition (face detection, medical imaging), natural language processing (chatbots, translation), and predictive analytics (forecasting, risk assessment).

For AI agent builders, machine learning is the foundation technology. LLMs are machine learning models trained on text data. The knowledge you don't need is the implementation details — platforms like Chipp handle the ML infrastructure so builders focus on crafting great agent experiences.