10 FREE RESOURCES THAT TURN A BEGINNER INTO AN AI ENGINEER

Bookmark this whole list. Follow it in order. This is the path people pay bootcamps $20,000 for, and every piece is $0.

1. https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python… START HERE. Harvard’s intro to AI with Python. Search, optimization, neural networks, and language models, taught from zero with real assignments. It gives you the vocabulary so nothing later sounds like a foreign language. Free, with a free certificate.

2. http://karpathy.ai/zero-to-hero THE FOUNDATION. Andrej Karpathy, founding member of OpenAI, builds a neural network from a blank file all the way up to a working GPT, line by line. By the end you understand backpropagation because you wrote it yourself. The single best deep learning course ever put online, and it costs nothing.

3. http://arxiv.org/abs/1706.03762 THE PAPER THAT STARTED IT ALL. “Attention Is All You Need.” Eight Google researchers wrote the eight pages that every model you use today is built on. Read it after Karpathy and it will finally make sense instead of looking like hieroglyphics.

4. http://github.com/karpathy/nanoGPT… YOUR FIRST REAL BUILD. The cleanest GPT training codebase that exists, about 300 lines. Clone it, train a small model on your own text, and watch it generate. This is where reading turns into building. MIT License.

5. http://course.fast.ai THE PRACTICAL LAYER. Jeremy Howard’s fast. ai teaches you to train state-of-the-art models in the first lesson, then explains the theory after you’ve already seen it work. The opposite order of school, and it sticks far better.

6. http://huggingface.co/learn/llm-course… THE INDUSTRY TOOLKIT. The library every AI company actually uses, taught by the people who maintain it. Loading models, fine-tuning on your own data, and shipping them. This is the gap between understanding AI and being employable in it. Free, no ads.

7. http://huggingface.co/learn/agents-course… THE FRONTIER SKILL. A free, certified course on building AI agents with smolagents, LangChain, and LlamaIndex. Agents are what every company is hiring for right now, and almost nobody has actually built one. You will have, by the end of this.

8. http://github.com/ollama/ollama YOUR LOCAL LAB. Run Llama, Mistral, DeepSeek, and Qwen on your own machine with one command. Stop paying per API call while you experiment. Every test you run here is free and private. MIT License.

9. http://github.com/mlabonne/llm-course… THE FULL MAP. A complete LLM engineering roadmap with notebooks, broken into the fundamentals, the scientist track, and the engineer track. When you don’t know what to learn next, this tells you. Apache 2.0.

10. discord and r/MachineLearning WHERE YOU STOP BEING ALONE. The fast. ai forums, the Hugging Face Discord, and r/MachineLearning are where people debug what they’re stuck on and read what shipped this week. The field moves monthly. A community is how you keep up after the courses end.

The expensive part of expertise was never the knowledge. It was knowing the order to learn it in.

CS50’s Introduction to Artificial Intelligence with Python | Harvard University

Source: https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python

What you’ll learn

• Graph search algorithms
• Reinforcement learning
• Machine learning
• Artificial intelligence principles
• How to design intelligent systems
• How to use AI in Python programs

Course description

AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly. This course will enable you to take the first step toward solving important real-world problems and future-proofing your career.

CS50’s Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

Enroll now to gain expertise in one of the fastest-growing domains of computer science from the creators of one of the most popular computer science courses ever, CS50. You’ll learn the theoretical frameworks that enable these new technologies while gaining practical experience in how to apply these powerful techniques in your work.

Instructors

Gordon McKay Professor of the Practice of Computer Science, Harvard John A. Paulson School of Engineering and Applied Sciences

Senior Preceptor in Computer Science, Harvard University

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