How to get started with AI: free and trustworthy material

The good news: nearly all the best material for learning AI is freely available and has years of community backing. Here is a map with short reviews and three itineraries, depending on where you are starting from.

The material

Starting from zero (no programming)

• Elements of AI — A University of Helsinki course, available in several languages including Spanish. The recommended starting point for non-technical readers: it explains the key concepts without heavy mathematics and with simple exercises.
• AI for Everyone (Andrew Ng, Coursera) — An overview of what AI is (and is not) and how it is applied in organisations. It can be audited for free, meaning you see all the content without the certificate.

Practical foundations (with Python)

• Kaggle Learn — Micro-courses of a few hours each (Python, pandas, intro to ML, deep learning) that run directly in the browser, with nothing to install.
• Google Machine Learning Crash Course — Google’s intensive course with videos and hands-on exercises. A Spanish version is available.
• fast.ai — Practical Deep Learning for Coders — One of the most respected courses out there. Instead of going from simple to complex, it has you training real models from the first lesson and works the theory in along the way.

Theory and fundamentals

• 3Blue1Brown — The “Neural networks” and “Essence of Linear Algebra” series are pure gold for building visual intuition. In English, with subtitles in several languages.
• Machine Learning Specialization (Andrew Ng) — The perennial classic, updated. Theory with just the right amount of mathematics. Also free to audit.
• MIT 6.S191 — Intro to Deep Learning — MIT’s annual intensive course: complete videos and slides, free and updated every year.
• Mathematics for Machine Learning — A free book (University of Cambridge) for catching up on algebra, calculus and probability.
• StatQuest — Short videos that explain statistics and ML without fuss. Ideal when something isn’t clicking.

Generative AI and language models

• Neural Networks: Zero to Hero (Andrej Karpathy) — Tesla’s former AI director builds and explains neural networks all the way up to a GPT from scratch. For many, the best free material for understanding how LLMs work on the inside.
• Hugging Face courses — The reference platform of the open source ecosystem: free courses on transformers, NLP, agents and more, with ready-to-run code.
• DeepLearning.AI short courses — One- or two-hour courses on specific topics: prompting, RAG, agents, and so on.
• Learn Prompting — An open source guide to working with language models.

In Spanish, and keeping up

• DotCSV — Carlos Santana’s channel, the reference for AI content in Spanish: serious explainers, interviews and news.
• IAAR online book — A sibling project to this guIA, with a more technical focus and in Spanish; its resources section is especially worth a look.
• Milone, D., Stegmayer, G., Ferrante, E., Fernández Slezak, D., Alonso Alemany, L., & Ferrer, L. (2022). ¿Aprendizaje automágico? Un viaje al corazón de la inteligencia artificial contemporánea (Universidad Nacional del Litoral) — Written by Argentine researchers, a rigorous and unsolemn introduction, made here and with examples from here.
• The Batch — DeepLearning.AI’s weekly newsletter, for keeping track of the field.

Itineraries

Route A — “I want to understand, not to program” Elements of AI (4 to 6 unhurried weeks) → AI for Everyone → Learn Prompting plus whichever short courses interest you → DotCSV or The Batch to keep up.

Route B — “I want to build things” Kaggle Learn (Python + pandas, if needed) → Kaggle’s Intro to ML → Google ML Crash Course → fast.ai → Hugging Face → Karpathy’s Zero to Hero. Roughly 3 to 6 months, practising in parallel. Tip: sign up early for a beginner Kaggle competition.

Route C — “I want solid foundations (towards university or research)” 3Blue1Brown (linear algebra and neural networks) → Mathematics for Machine Learning → Ng’s ML Specialization → MIT 6.S191 → then, depending on the area: CS231n (vision), CS224n (language) or CS229 (classical ML); the Stanford lectures are on YouTube. With Dive into Deep Learning as your reference book.

Route D — “I want to understand what is at stake” Any of the three above, crossed from the outset with the third entry of this chapter. It is not a later route or an optional add-on: it runs in parallel, because questions about what a model is for become far harder to formulate once you have learned to build one without asking them.

Closing advice

• English: most of the material is in English. YouTube’s subtitle translation is decent, and you get to practise technical English along the way.
• Coursera for free: when enrolling, look for the “audit course” option; only the certificate is paid.
• Actually practise: watching lectures is not enough; do the exercises and build your own projects, however small.
• Be wary of expensive paid shortcuts: the essentials are free. Pay only once you know exactly what you are missing and why.
• Notice who is teaching: almost this entire canon comes from a handful of universities and companies in the North, and that is not neutral with respect to which problems count as interesting. That is no reason not to use it —it is the best available— but it is a reason to read it knowing where it is written from.

The three routes above will keep you going for months. When they run out, or when you want to go deep in one particular area, the next entry picks up everything that did not fit here: the reference books, the specialisations by field, the blogs worth following and the communities where this gets argued out.

Note: links may change over time; if one doesn’t open, search for the name of the resource.