💡 Machine Learning (ML)

Machine Learning (ML)#

Algorithms that learn patterns from data rather than being explicitly programmed.

Important

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What it is#

Machine learning (ML) is the branch of AI in which computers learn patterns from data rather than following hand-written rules. You supply examples, the model learns the relationship between inputs (features) and outputs (labels), and once trained it predicts on new, unseen data.

The core idea#

Formally, ML fits a function

\[y = f(X) + \varepsilon,\]

where $X$ are the input features, $y$ the output, $f$ the function learned from data, and $\varepsilon$ irreducible noise. Learning means estimating $f$.

The kinds of learning#

Supervised learning uses labelled data — regression for continuous targets (house price), classification for categories (spam or not). Unsupervised learning works on unlabelled data — clustering (customer segmentation) and dimensionality reduction (PCA, embeddings). Semi-supervised mixes a little labelled with much unlabelled data (costly medical labels). Reinforcement learning has an agent learn from rewards by acting in an environment. And self-supervised learning predicts part of the input from the rest (masked words) — the engine behind modern LLMs.

Workflow and an example#

The lifecycle is collect → clean → choose a model → train → evaluate → deploy → monitor and retrain. Train a model on thousands of houses — 1,000 sqft and 3 rooms sold for $250,000 — and it learns that price rises with size and rooms, so a new 1,200 sqft, 4-room house is predicted at roughly $300,000. ML matters because it automates pattern discovery at a scale and complexity beyond hand-coded rules.

Theme: AI & ML Concepts · All terminology

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Mind map — connected ideas

AI (Artificial Intelligence) · Neural Networks · Customer Segmentation · Embedding · Regression Coefficient · Medical AI

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More in AI & ML Concepts

AI (Artificial Intelligence) · Classification Models · Computer Vision (CV) · Decision Trees · Linear Models · LLMs (Large Language Models) · Logistic Regression · Medical AI · Natural Language Processing (NLP) · Neural Networks · Regression Models · Support Vector Machines (SVMs) · Target Variable