From Padawan to Jedi Master to Whatever Yoda Was: A Curated List of AI Learning Resources
If you’re a computer science student, early in your career, or interested in tech, I built AI & Your Tech Career for you. It addresses the biggest questions and concerns regarding AI’s impact on the field. Skills to learn, tools to use, free courses, great readings, and student perks. Check it out at aka.ms/nocap.
This page is an ongoing list of learning resources for AI, ranging from stuff to get you started to stuff for diving deep. My goal is for this to be a targeted collection, quality over quantity. I started it for myself, a software engineer looking for ways to use AI in my work, and figured others might find it useful. None of these are affiliate links.
While being undeniably so hot right now, AI education isn’t exactly a novel field. However, the overwhelming demand for content in the space is certainly new. This list is my attempt at separating the wheat from the chaff.
Nerd Note: If you read the title of this article and went “What do you mean whatever Yoda was? Yoda was no mere Jedi Master, he was a Grand Master!”, you’re a nerd, and I love you. Yes, Yoda was a Jedi Grand Master, but it could be said he reached a level of power, influence, and status that went far beyond a title he shared with several counterparts. Yoda’s Jedi lineage is insane. He is a master of masters many generations over. The Star Wars equivalent of Abraham. I believe he should have his own category, Chief Grand Master or something, especially considering his ability to remain prudent despite his infamous ketamine addiction.
Wading In
Most people have heard of AI in some way (ChatGPT, Copilot, Gemini, Claude, Midjourney, Skynet), but if you want to get started with understanding what’s going on under the hood, here are some good resources.
Layman’s Terms
To start, it’s worth getting familiar with the various terms and concepts associated with AI. There’s a flurry of “new” (to the general public) AI terminology floating around these days. To actually understand it all, it helps to read resources that break things down into layman’s terms. Less trying to sound smart and more trying to actually get through to people. The items below do exactly that!
Microsoft: 10 AI Terms Everyone Should Know & 10 More AI Terms Everyone Should Know
These articles from Microsoft walk through a total of 20 terms that are fundamental to the AI field. They each stand out for being written in an approachable beginner-friendly way. Reading and understanding the content within them will help you speak confidently about AI (and sound smart to your friends) while navigating your learning journey.
Free or Paid: Free.
MIT Sloan: How ChatGPT Works: A Non-Technical Primer
This free video lecture from MIT Sloan, led by Professor Rama Ramakrishnan, dives into how AI models like ChatGPT actually work. It covers the basics, like the “predict the next word” mechanism that drives these models, the massive datasets they’re trained on, and the neural networks that give them their conversational superpowers. You’ll also get a behind-the-scenes look at how GPT evolved from version 3 to ChatGPT, the surprising abilities it developed along the way, and what’s being done to tackle issues like bias and nonsensical outputs.
Free or Paid: Free.
Parand Tony Darugar: A Completely Non-Technical Explanation of AI and Deep Learning & A Non-Technical Explanation of ChatGPT & A Non-Technical Explanation of ChatGPT: Deep Learning
There’s a wonderful series of articles by Parand Tony Darugar that break down AI, machine learning, deep learning, ChatGPT, and related topics in clear, layman-friendly terms. As a seasoned technologist and serial entrepreneur, Darugar offers down-to-earth insights for anyone looking to truly understand how generative AI works without the jargon and posturing. I developed an entire student-centric workshop based on how well he explains things in these articles, you can check it out here.
Free or Paid: Free.
3Blue1Brown: Large Language Models Explained Briefly
Short visual video from 3Blue1Brown that breaks down how LLMs predict the next word and get trained. Great for a foundational understanding of how modern AI works.
Free or Paid: Free.
MIT Technology Review (Will Douglas Heaven): Why Does AI Hallucinate?
Plain-English article on why chatbots make stuff up and sound so sure of themselves while doing it.
Free or Paid: Freemium (free monthly articles, with a metered paywall after that).
Segun Akinyemi (Me): What Is AI Engineering?, MCP vs RAG, & Agentic AI: From Acronyms to Applications
My own posts explaining some of the biggest AI buzzwords in plain language.
Free or Paid: Free.
Intro Courses
These courses walk you through the basics of AI. By basics, I mean the basics of what’s happening behind the scenes, rather than the basics of using AI, such as signing up for an account to use ChatGPT. It’s possible to be a decent enough AI user without understanding the concepts covered in these courses. Like, you don’t need to understand automotive engineering to drive a car, but if you want to be a better driver, understanding how your vehicle fundamentally works is undoubtedly a plus.
Anthropic: AI Fluency: Framework and Foundations & OpenAI: AI Foundations
Free, self-paced intro courses from the creators of Claude and ChatGPT on how to work with AI, give clear context, judge its outputs, and know when not to trust it. What you learn in both applies to any AI tool.
Free or Paid: Free.
Microsoft: AI for Beginners & Google: Introduction to Generative AI
Both Microsoft and Google have entry-level “get started with AI” courses that are solid. Each company also has robust online education platforms, namely Microsoft Learn and Google Skills. They offer the kind of free knowledge and training that makes me question if anyone really needs 4 long years of undergraduate school to break into tech these days, but that’s a topic for a different day.
Free or Paid: Free.
Running AI Locally
You don’t have to pay a monthly subscription or send your private files to a tech company’s cloud just to use AI. If you have a decent laptop or desktop, you can download AI models and run them on your own computer for free, offline, and in private.
LM Studio and Unsloth are the leaders for running AI models locally. They both do the same things and have slightly different UIs, so choose whichever looks or feels better to you and it’ll get the job done. If you’re a developer who wants to wire local models into your coding tools, check out Using Local LLMs for Agentic Coding.
Free or Paid: Free.
Swimming Swiftly
If you’re ready to go past the basics and get into building, customizing, and practically using AI, here are some resources to help you out. They aren’t quite as deep as dropping everything to become a full-time AI Engineer, but they’re sufficient for a tech-savvy individual to gain the knowledge needed to be dangerous.
Context Engineering
The field that used to be called Prompt Engineering (a rather generous use of the word engineering) has evolved into Context Engineering. Instead of just typing clever sentences into a chat box and hoping for the best, Context Engineering is about giving the AI the right background information, instructions, documents, and tools so it can produce useful results.
GitHub Blog (Christina Warren): Want Better AI Outputs? Try Context Engineering. & Philipp Schmid: The New Skill in AI Is Not Prompting, It’s Context Engineering
Prompt engineering is dead. Typing clever sentences into a chat box has been replaced by context engineering, which is giving the AI the right information, instructions, and tools when it needs it the most. These two reads explain that shift in plain terms.
Free or Paid: Free.
Anthropic: Effective Context Engineering for AI Agents & Hugging Face: The Context Course
Best suited for people comfortable with code. Anthropic’s article is the best breakdown of how context works in real systems (also see Drew Breunig’s How Long Contexts Fail), and Hugging Face’s free course walks you through skills, MCP, plugins, subagents, and hooks hands-on.
Free or Paid: Free.
Large Language Models
A large language model (LLM) is a type of AI trained on massive amounts of text data. This training enables it to understand and generate content in response to instructions (prompts). An example is OpenAI’s GPT (Generative Pre-trained Transformer), where Generative highlights its ability to create new content, Pre-trained indicates it has been trained on a wide array of text data, and Transformer refers to the model’s architecture, designed to handle complex patterns in data efficiently. Understanding LLMs is key to moving beyond the basics and towards AI expertise.
Andrej Karpathy: Intro to Large Language Models & Deep Dive into LLMs like ChatGPT
Andrej Karpathy is an AI heavyweight. Just read through his Wikipedia page and you’ll understand why. He was one of OpenAI’s co-founders and, in addition to being a brilliant engineer, is an excellent educator, dropping AI knowledge for free on his YouTube channel. His 1-hour Intro to Large Language Models and his longer Deep Dive into LLMs like ChatGPT come highly recommended by engineers around the web. I see them linked in “How do I learn AI” threads all the time. They’re certainly worth a watch!
Free or Paid: Free.
Hugging Face: LLM Course & Smol Course
Hugging Face’s free courses walk you through how large and small language models work under the hood, and Pamela Fox’s Visual Exploration of Vector Embeddings and Interactive Comparison let you see for yourself how training data shapes what a model knows.
Free or Paid: Free.
AI in Software Engineering
AI has changed how software is built, but it hasn’t removed the need to understand what’s happening under the hood. These resources and courses cover what software engineering looks like with AI and how to build with these tools responsibly.
Mark Russinovich & Scott Hanselman: Redefining the Software Engineering Profession for AI & Microsoft + Carnegie Mellon University: PRAISE Program
Legends Mark Russinovich and Scott Hanselman explain how AI is changing software development, why programming/coding is not software engineering, and how Microsoft and Carnegie Mellon are training new developers to build judgment instead of blindly trusting AI. I cannot emphasize enough how much I feel every educator everywhere teaching computer science should read this!
Free or Paid: Free.
MIT: The Missing Semester of Your CS Education
Step-by-step lectures, videos, and hands-on exercises teaching the practical developer tools that CS programs (at non-target schools) usually skip (shell, Git, debugging, testing, environments), updated in 2026 with AI tools folded into every lecture.
Free or Paid: Free.
Anthropic: Claude Code in Action, OpenAI: Codex 102, & GitHub: Getting Started With GitHub Copilot
Free, hands-on courses anyone can take from start to finish to learn how to use three of the bigger AI coding harnesses. Also check out the public syllabi and slides from Stanford’s The Modern Software Developer, Carnegie Mellon’s Effective Coding with AI, and Cornell’s Software Engineering in the Era of AI to see what top universities are teaching in class.
Free or Paid: Free.
Agentic AI
Agentic AI is AI that can act independently and make decisions. Which is a scary thing, and requires humans in the loop so it doesn’t make mistakes. And that isn’t something that changes when they release a new model. Reality is complicated. It’s nuanced. There is no probability based AI model that’ll change that. Agentic AI literally doesn’t work without a human involved. You can’t fire all your staff, they’ve tried, it doesn’t work.
To demonstrate, Generative AI vs. Agentic AI is the difference between “Hey AI, how do I book a flight?” and “Hey AI, book me the cheapest flight to Lagos next Tuesday”. I’m sure you can imagine the many things that could go right, or wrong, now that we’ve started letting AI systems make decisions and access stuff that goes beyond providing information. Agentic AI is a big deal, and it’ll do you good to be knowledgeable about it. Here are some resources for learning more!
Microsoft: AI Agents, What They Are, and How They’ll Change the Way We Work
Digestible breakdown of what AI agents are and why they’re important. Even as the years roll on, this is a good read as it captures our reaction to Agentic AI first hitting the scene and a definition of it that stands the test of time.
Free or Paid: Free.
Microsoft: AI Agents for Beginners
Free course to help you understand AI Agents with lessons that handhold you through learning. The course is backed by a GitHub repository with all the resources.
Free or Paid: Free.
Hugging Face: AI Agents Course
Hugging Face, the GitHub of AI models, has created a great free course that takes you through AI Agents from a technical standpoint. So, if you want to be involved in building them, this is for you. If you’re not familiar with Hugging Face, they’re the creators of an open-source platform that’s become central to the democratization of AI. All sorts of models from everyone and everywhere in one place.
Free or Paid: Free.
Hugging Face & Anthropic: Model Context Protocol (MCP) Course
Without APIs and MCP, AI agents can’t do much. This free course from Hugging Face and Anthropic teaches how MCP connects AI models to real tools and data.
Free or Paid: Free.
Simon Willison: The Lethal Trifecta for AI Agents, Meta AI: Agents Rule of Two, & MIT Technology Review (Margaret Mitchell et al.): Why Handing Over Total Control to AI Agents Would Be a Huge Mistake
The AI we’ve created is not alive and you shouldn’t treat it as though it is. These articles explain why you should be really careful with what you give AI agents access to. Humans must always remain in the loop.
Free or Paid: Free.
Diving Deep
If you’re set on making AI your bread and butter, then this stuff is your jam 🙃. This is “I want to be a Machine Learning Engineer”, and/or “I want to help advance humanity towards artificial general intelligence” territory.
It’s beyond what’s needed for practical application. The earlier resources are sufficient for being the “AI person” at most companies. However, if you really want to know your stuff when it comes to AI, like going from the “car guy” to the “creator of novel automotive engineering advancements guy”, these resources will help.
Again, this is way deeper than most people need. Like, even for me as a software engineer at a company you’ve heard of, this is beyond what I need to practice AI Engineering. And yet, if you’re the kind of person that likes going deep, this is for you.
Foundational Texts
It might be all the rage right now, but AI isn’t new. There have been several landmark works (many of them originating from researchers at Google, who deserve their flowers for advancing the field, while Google itself deserves shame for squandering their lead) that redefined what machines could do. To understand AI, from first principles, you need to go back and read the papers, blogs, books, etc. that laid the foundation for our present AI-crazed world. Here are a few worth reading. Remember, these resources are for diving deep, way deep. These aren’t light reads.
Alan Turing: Computing Machinery and Intelligence
Ever heard of the Turing Test? The test for whether a machine has reached a level of intelligence indistinguishable from a human being. This paper, written by legendary mathematician and computer scientist Alan Turing, is where it was first introduced. Published in 1950, it not only proposed the Turing Test but also delved into the possibilities and implications of what we now call artificial intelligence. Yes, this paper is old, but if you’re trying to “know your stuff” when it comes to AI, reading its 28 or so pages is a must.
Free or Paid: Free.
Google Brain: Attention Is All You Need
Published in 2017 by Google Brain, the paper Attention Is All You Need introduced the transformer model, laying the foundation for modern generative AI. That is, the discoveries introduced in this paper are directly responsible for OpenAI’s later breakthrough with ChatGPT. Its revelations shifted AI from rigid, sequence-based processing to a flexible framework capable of understanding and generating human-like text. It’s a seminal read for anyone looking to understand how we got to our current state of AI-generated content being convincing.
Free or Paid: Free.
Google Research: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
In 2018, Google Research had yet another big discovery with the introduction of BERT (Bidirectional Encoder Representations from Transformers). The paper describes a method that allows AI to better comprehend the nuances and context of language as it’s used in the real world. BERT set a new standard for natural language processing tasks, such as answering questions, translating languages, and summarizing longform text. All of those learnings have become critical parts of major AI tools (ChatGPT, Claude, Gemini, etc.).
Free or Paid: Free.
Stuart J. Russell & Peter Norvig: Artificial Intelligence: A Modern Approach
Do you remember when video games (excluding Madden and NBA 2K) ditched the “release a new game every year” format in favor of the “release a game and support it for years with DLCs and bug fixes” format? That’s what this book is for AI. It is the AI textbook across universities globally and has been since its original publication in 1995. Despite its ostensibly ancient publication date, it’s nowhere near outdated. The authors periodically release new editions to keep with the times, each one improving on the last. If you want to dive deep into AI, make this your swimming pool.
Free or Paid: Technically speaking, paid, but actually, free if you know where to look.
Advanced Concepts
Machine Learning, Neural Networks, and Deep Learning are all important domains of artificial intelligence. Understanding what they are, and the differences between them, is key to building AI expertise. Here’s an intentionally oversimplified rundown of those terms.
- Machine Learning is the broad field of teaching computers to learn from examples rather than following step-by-step instructions. You give it data and let it figure out patterns on its own.
- Neural Networks are a way to teach computers to recognize patterns, inspired by how the human brain works. They pass data through layers, each one building on the last, going from simple details to more complex patterns.
- Deep Learning is just Neural Networks with lots and lots of layers, allowing the model to learn detailed patterns across huge amounts of data.
This image from Data Camp does an excellent job of breaking down the terminology and their relationships.
What follows are specific learning resources for these advanced subdomains of AI.
Machine Learning
Notable learning resources for diving into Machine Learning.
Google: Machine Learning Crash Course
When it comes to AI education, Google’s resources are top-tier, and for good reason. Let’s not forget that Google invented Transformers (the “T” in GPT), which revolutionized the AI landscape. Their Machine Learning Crash Course is designed to get you up to speed quickly with the fundamentals and practical applications of ML. For those with more time on their hands, Google’s broader Machine Learning education portal offers even more in-depth materials.
Free or Paid: Free.
Kaggle: Kaggle Learn
Short courses where you write (or have AI write and you review) code right in the browser to learn machine learning for free. Pair it with the r/learnmachinelearning subreddit, a large community of beginners, students, and professionals talking about and learning machine learning.
Free or Paid: Free.
Neural Networks
Notable learning resources for diving into Neural Networks.
Andrej Karpathy: Neural Networks - Zero to Hero
To thoroughly explore and understand Neural Networks, there are few better resources than this video series by Andrej Karpathy. He’s one of OpenAI’s co-founders, a former director of AI and Autopilot Vision at Tesla, and an all-around brilliant computer scientist. The YouTube playlist for the series can be found here, but Andrej also has a website for the course.
Free or Paid: Free.
3Blue1Brown: Neural Networks Video Series
If you’re a visual learner, this is the neural networks resource for you. It was created by 3Blue1Brown, one of the most respected educational channels on YouTube, and breaks down how neural networks work with impressive visuals and animations. It’s one of the most frequently recommended resources on developer forums like Hacker News and r/MachineLearning, and for good reason. Seeing the math come to life helps makes all the abstract AI concepts click. If you’d rather read than watch the videos (they’re long), there’s a written and interactive form of the series here.
Free or Paid: Free.
Deep Learning
Notable learning resources for diving into Deep Learning.
Fast AI: Practical Deep Learning
If you already have coding experience and want to skip straight to practical applications of Deep Learning, this free course created by fast.ai is a good fit. It’s less “reasoning from first principles” and more “here’s what to know so that you can use this right now”.
Free or Paid: Free.
Christopher Bishop & Hugh Bishop: Deep Learning: Foundations and Concepts
Christopher Bishop, a Microsoft Technical Fellow and director of Microsoft Research AI for Science, has been doing AI work since before it was mainstream. His book, Deep Learning: Foundations and Concepts, covers modern deep learning and transformers (so a post-LLM world) and is free to read online on its official site.
Free or Paid: Free.
Conclusion
There’s a lot of content out there related to AI. With this post, I’m trying to pare things down to what’s uniquely worth spending time on. Time is precious, and now more than ever, curated content that gets to the heart of what you’re looking for is needed to make the most of it.
There are resources on this page that I haven’t even finished combing through. I relied heavily on suggestions from AI experts and tech-savvy users on Hacker News, Reddit, and Stack Exchange while compiling this list.
I’m keeping it updated as I find new resources, or conclude that existing resources aren’t worth keeping listed. Feel free to share your thoughts with me using my contact page or the comment section at the bottom. I hope you got some value out of this article!
