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.

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

By Federico Calò — Software Developer & Technical Writer