# 01 - Introduction to Machine Learning: Supervised, Unsupervised, and Reinforcement Learning

Learn what Machine Learning is, the 3 fundamental types (supervised for predictions, unsupervised for patterns, reinforcement for decisions), real-world use cases, and a roadmap to choose the right paradigm for your problem.

## What you'll learn

- Machine Learning enables computers to learn from data without explicit programming
- Three paradigms: supervised (labeled data), unsupervised (no labels), reinforcement (rewards)
- The ML workflow includes: data collection, preprocessing, feature engineering, training, evaluation, deployment
- scikit-learn is the ideal starting point with a consistent API (fit, predict, score)
- Paradigm choice depends on data type and the problem objective

*This article is part of the **Machine Learning** series on federicocalo.dev.*

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## Read the full article

The complete article (13 min read) with code examples, diagrams, and practical exercises is available here:

**➡️ [01 - Introduction to Machine Learning: Supervised, Unsupervised, and Reinforcement Learning](https://federicocalo.dev/en/blog/machine-learning-introduction-supervised-unsupervised-reinforcement)**

`https://federicocalo.dev/en/blog/machine-learning-introduction-supervised-unsupervised-reinforcement`

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*By [Federico Calò](https://federicocalo.dev) — Software Developer & Technical Writer*