Learning AI | guIA

How to get started with AI: free and trustworthy material

Sun, 30 Aug 2026 00:00:00 +0000

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.

Note

This map covers mostly the technical side, which tends to be the best documented and the worst contextualised. If you want more of the same kind of thing, continue to ; if you want the counterpart —who produces this knowledge, for whom, and from where— it is in . And if you landed straight here, the that open the chapter answer what it is you are about to learn to build.

The material

Starting from zero (no programming)

— 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.
(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)

— 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’s intensive course with videos and hands-on exercises. A Spanish version is available.
— 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

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

Generative AI and language models

(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.
— The reference platform of the open source ecosystem: free courses on transformers, NLP, agents and more, with ready-to-run code.
— One- or two-hour courses on specific topics: prompting, RAG, agents, and so on.
— An open source guide to working with language models.

In Spanish, and keeping up

— Carlos Santana’s channel, the reference for AI content in Spanish: serious explainers, interviews and news.
— A sibling project to this guIA, with a more technical focus and in Spanish; its section is especially worth a look.
Milone, D., Stegmayer, G., Ferrante, E., Fernández Slezak, D., Alonso Alemany, L., & Ferrer, L. (2022). (Universidad Nacional del Litoral) — Written by Argentine researchers, a rigorous and unsolemn introduction, made here and with examples from here.
— 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: (vision), (language) or (classical ML); the Stanford lectures are on YouTube. With 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 . 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.

Continue with Going further

Going further

Sun, 30 Aug 2026 00:00:00 +0000

If you arrived from the previous entry, here are the resources that did not fit into the three itineraries: deeper, more specific or more niche, but with the same criteria as always — free and well regarded. None of this is compulsory: it is a menu, not a checklist.

Free reference books

— The most recommended modern deep learning book of recent years: clear, beautifully illustrated, free PDF.
— The classic “bible”. Denser and from 2016, but still the reference for theoretical grounding.
— An ultra-condensed ~160-page summary, designed to be read on a phone. Ideal for revision.
— The classic of statistical ML, now with Python labs. Unbeatable for really understanding the “traditional” models.
— Two volumes (intro and advanced) in open draft: the encyclopaedic reference for probabilistic ML.
— An interactive online book where you build a network from scratch. A classic that pairs perfectly with 3Blue1Brown.

Specialising by area

Reinforcement learning

— The bible of the field, with a free official PDF.
— The lectures that trained a generation; on YouTube, they accompany the book.
— Theory, code and practical advice for getting started in earnest.
— Less theoretical, more hands-on: you train agents from the first unit.

NLP and LLMs

— THE NLP textbook, in open and updated draft. The theoretical complement to CS224n.
— Recipes and example code for building with LLMs: RAG, agents, evaluation and more.
— Free tutorials from the team behind Claude, including their interactive prompt engineering course.

MLOps and production

— Design, development and deployment of ML systems, with a focus on good practice.
— A free course with cohorts, projects and a very active community.
— The pioneering course on taking models to production. No longer updated frequently, but the material remains useful.

Ethics, justice and safety

— An open book on algorithmic bias and discrimination. Serious and accessible.
— A free, direct course on disinformation, bias, privacy and accountability, written by someone who also teaches the technical side.
— Free courses (with a selection process) on AI safety and alignment; highly regarded in that niche.

Tip

This subsection is the hinge with the . “AI ethics” as taught in these courses is above all an ethics of implementation —how to audit a model, how to measure a bias—; questions about the political economy, the infrastructure and the geography of the field are taken up there.

More fundamentals (if you want to shore things up)

— The most famous linear algebra lectures in the world.
— Probability explained with clarity and memorable examples.
— If you need to start further back, here is the complete path (also ).
— The best-known introduction to programming, entirely free.
— Terminal, git, debugging and all those tools nobody teaches you and you use every day.
— Programming practice with free certifications; includes an ML track with Python (also available ).

Blogs that are pure gold

— His “The Illustrated Transformer” and “The Illustrated GPT-2” posts are the visual explanation of LLMs.
— Technical, exhaustive posts on agents, diffusion, RLHF and more. Among the best writing in the field.
— Interactive articles of rare beauty. It stopped publishing in 2021, but the archive is still a goldmine.
— Few posts, all memorable (start with “Understanding LSTMs”).
— Don’t miss “A Recipe for Training Neural Networks”: practical advice distilled from years of craft.
— One of the sharpest voices on ML systems; her blog and Stanford notes are public.

YouTube for papers and news

— Paper reviews with sharp humour; the most entertaining way to start reading research.
— In-depth debates with front-line researchers.
— Quick explainers of the latest; more for inspiration than for learning in depth.
— Short series with exceptional pedagogical and production quality.
— Seminars where the researchers themselves present the latest work.

Keeping a finger on the pulse

— A weekly newsletter from one of Anthropic’s founders: advances, context and policy.
— Technical analysis of what’s new in LLMs, written by a researcher and teacher.
— The annual reference report on the state of AI.
— Where everything is published first. Look at the cs.LG, cs.CL and cs.AI categories.
— The papers the community is discussing today, ranked by trend.

Communities

and — Discussion, news and help for beginners.
— Very active for implementation questions.
and — High-quality Q&A, especially on the statistical side.
— An open source research community; its Discord is a hive of activity.

Extending the itineraries

If you took Route A (understanding without programming): Nielsen’s Neural Networks and Deep Learning → Jay Alammar’s The Illustrated Transformer → fairmlbook → Two Minute Papers or Import AI to keep up.

If you took Route B (building things): The Missing Semester → An Introduction to Statistical Learning → MLOps Zoomcamp or Made With ML → the OpenAI Cookbook and the Anthropic courses for your day-to-day with LLMs.

If you took Route C (solid foundations): Probabilistic Machine Learning or Understanding Deep Learning → Sutton and Barto with Silver’s course (if RL grabs you) → Lilian Weng → arXiv and Hugging Face Papers for reading research. Optional: BlueDot if AI safety interests you.

One piece of advice for starting to read papers

Don’t start with the bare PDF. First look for a post or video explaining it (the blogs and channels above help a great deal), read the abstract, figures and conclusions, and only then get into the body. If you want a formal method, S. Keshav’s classic proposes the “three passes” and takes fifteen minutes to read.

One thing remains to be said about all this material, and it is what Route D was pointing at. The courses, books and blogs in these two entries came almost entirely out of a handful of universities and companies in the global North. That does not invalidate them —again: it is the best that is freely available— but it does decide in advance which questions count as interesting. The next entry is the other half of the itinerary.

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

Continue with Thinking AI from the Global South

Thinking AI from the Global South: a critical itinerary

Sun, 30 Aug 2026 00:00:00 +0000

The resource map and its extension assemble a technical itinerary produced almost entirely in a handful of universities and companies in the global North. That does not invalidate them —by a wide margin, it is the best free material available— but it does determine what gets taught as “the problem”: how to improve accuracy, how to scale training, how to deploy to production. Almost never: with what data, extracted from whom, with what energy and what water, under what labour regime, and who ends up in a position to decide about any of it.

This entry assembles the other half of the itinerary. It is not “the ethics part” at the end of the course: it is a set of conceptual tools that change what you see when you look at a model. And it is organised with an explicit bias —Latin America and the Global South— not as a victim category or an audience to consult, but as a site where concrete institutional alternatives are already being produced.

Frameworks for seeing AI as infrastructure, not as algorithm

Crawford, K. (2021). Atlas of AI — Follows AI backwards, from the interface to lithium, water, labelling labour and data archives: the best entry point for moving from thinking about “algorithms” to thinking about planetary supply chains.
(Kate Crawford and Vladan Joler) — A navigable, free visual genealogy of five centuries of technologies of calculation, communication, classification and control. Useful both for study and for teaching: a single enormous image where you can see that none of this began in 2022.
Couldry, N. and Mejias, U. (2019). The Costs of Connection — The most influential formulation of the data colonialism thesis: not a metaphor about colonialism, but the claim that a new appropriation —that of social life converted into data— has a structure analogous to the colonial appropriation of land and labour. Their research network, , publishes and organises largely from Latin America.
Birhane, A. (2020). — Short, forceful and open access. It shows how promises to “solve African problems with AI” reproduce infrastructural dependency and a model in which the continent supplies data and market and receives solutions designed elsewhere. Probably the single best text for understanding what is meant by “algorithmic colonialism”.
Mohamed, S., Png, M.-T. and Isaac, W. (2020). — Written from inside the industry, it translates decolonial theory into concrete tactics for AI research practice: reverse design, caution about “beta-testing” on vulnerable populations, critical solidarity.
Ricaurte, P. (2019). Data Epistemologies, The Coloniality of Power, and Resistance — From Mexico, it articulates data extraction with the coloniality of power and knowledge and —most interestingly— with the practices of resistance that already exist in the region.
Mhlambi, S. (2020). From Rationality to Relationality: Ubuntu as an Ethical and Human Rights Framework for AI Governance — Proposes replacing the individual rational subject underlying almost all AI ethics with a relational ontology (ubuntu). It is the clearest example that “epistemologies of the South” does not mean applying the same frameworks with different examples, but changing the frameworks.
— A short, collective, multilingual text. Useful for opening a discussion in a class or a team.

Algorithmic justice, bias and its limits

— Founded by Joy Buolamwini. Beyond the research, it has material designed for action: don’t miss her and the documentary , available on several platforms.
— The independent research institute founded by Timnit Gebru after her departure from Google. AI research done explicitly outside the agenda of the large companies, with teams distributed across several continents.
— The open book by Barocas, Hardt and Narayanan. Technical rigour about what can and cannot be measured as “bias”; its final chapter is a good vaccine against the idea that fairness is an optimisation problem.
(Rachel Thomas, fast.ai) — The ethics course written by someone who also teaches the technical side; especially good on disinformation and on the feedback mechanisms of recommender systems.
(Casey Fiesler) — Hundreds of tech ethics syllabi in an open spreadsheet. If you have to build a course, start here.

Political economy and philosophy of technology

Zuboff, S. (2019). The Age of Surveillance Capitalism — The obligatory, if contested, reference: behavioural surplus as raw material. For a quick way in, there is and its .
Frenken, K. — — A lecture from before the enthusiasm cycle of 2022 onwards, and useful precisely for that: the analysis of the platform economy holds up without the noise of the present.
Floridi, L. (2023). The Ethics of Artificial Intelligence — The best summary (and position-taking) of the literature accumulated since around 2014, when much of the social problematic of AI began to settle around the question of ethics and good practice. Useful precisely for understanding the framework that the texts in the previous section argue with.
Coeckelbergh, M. (2020). AI Ethics (MIT Press, Essential Knowledge series) — Brief, orderly and a good map of the philosophical debate.
Frischmann, B. and Selinger, E. (2018). Re-Engineering Humanity — The inverted question: not whether machines become human, but to what extent the design of technical environments makes us more machine-like.
On techno-optimism and techno-pessimism — Marc Andreessen’s is worth reading as a document: not for the quality of its argument, but because it lays out with unusual frankness the ideology of much of the venture capital funding this field. Read it alongside Gómez, R. J. (1997), Progreso, determinismo y pesimismo tecnológico (Redes, 4(10)), which dismantles the false dilemma from within Argentine philosophy of science.

Videos to think with (and to teach with)

Zeynep Tufekci on why machine intelligence makes human morals more important, not less:

Sasha Luccioni on the concrete, present harms —environmental cost, bias, invisible labour— as against speculative risks:

Where this is being thought in Latin America

Research groups and organisations

— A research group in philosophy of technology; it organises annual workshops on AI, several of them at the .
— An Argentine open working space on AI.
— Sustained work from Córdoba on digital rights, free software and, in recent years, bias and discrimination in language models, with tools designed so that non-technical communities can audit stereotypes in models.
— A regional organisation (based in Chile) producing some of the best Spanish-language research on AI in the state, surveillance and human rights in Latin America.
— From Brazil, it crosses technology, gender and rights with a design and public-intervention approach.
— The Latin American Network of Surveillance, Technology and Society Studies: conferences, dossiers and a consolidated regional academic community.
— Chile’s National Center for Artificial Intelligence; among other things, it coordinates the Latin American Artificial Intelligence Index (ILIA) and the effort to train regional language models with Latin American data and participation.
— Not Latin American, but the most cited example of what this chapter discusses: a pan-African, distributed, grassroots community building NLP for African languages without waiting for a company in the North to decide it is worthwhile.

Curricular spaces

Regional technical community

— The Latin American meeting in artificial intelligence: an intensive research school that rotates among cities in the region.
— A network and mentoring programme; it organises workshops at the field’s major conferences.
and — The open access publishing infrastructure built in the region, without article processing charges. They belong here for a precise reason: they are proof that Latin America has already built epistemic commons at continental scale, and therefore that the question of a public, regional AI infrastructure is not utopian but budgetary and political.

Events: 2nd AI Conference at Universidad Nacional de Córdoba (December 2024)

Full programme on the (in Spanish).

Day 1

Day 2

Route D, in order

If you want an itinerary rather than a catalogue:

See the terrain: Crawford’s Atlas of AI, with Calculating Empires open alongside.
Name the structure: Birhane, Algorithmic Colonization of Africa → Mohamed, Png and Isaac, Decolonial AI → Couldry and Mejias.
Change the framework, not just the examples: Mhlambi (ubuntu), Ricaurte, Santos.
Land on what can be measured: fairmlbook and Practical Data Ethics, so as not to be left with critique and no tools.
Find where it is already being done: Vía Libre, Derechos Digitales, Masakhane, SciELO/AmeliCA. This step matters: without it, critique becomes an elegant description of a defeat.

And if you are coming from the technical side, the reverse order works just as well: start at step 5 —with a concrete organisation doing something— and work back up to the frameworks.

So far the chapter has worked on AI in general: what it is, how to learn it, who produces it and at whose expense. But the same technology changes shape depending on where it lands, and the questions become different ones when there is a classroom, a hospital or a public office on the other side. That is the next chapter.

Learning AI | guIA

How to get started with AI: free and trustworthy material

The material

Starting from zero (no programming)

Practical foundations (with Python)

Theory and fundamentals

Generative AI and language models

In Spanish, and keeping up

Itineraries

Closing advice

Going further

Free reference books

Specialising by area

Reinforcement learning

NLP and LLMs

MLOps and production

Ethics, justice and safety

More fundamentals (if you want to shore things up)

Blogs that are pure gold

YouTube for papers and news

Keeping a finger on the pulse

Communities

Extending the itineraries

One piece of advice for starting to read papers

Thinking AI from the Global South: a critical itinerary

Frameworks for seeing AI as infrastructure, not as algorithm

Algorithmic justice, bias and its limits

Political economy and philosophy of technology

Videos to think with (and to teach with)

Where this is being thought in Latin America

Research groups and organisations

Curricular spaces

Regional technical community

Events: 2nd AI Conference at Universidad Nacional de Córdoba (December 2024)

Route D, in order

Suggested reading