fast.ai's Practical Deep Learning for Coders is a free course teaching deep learning through code first (top-down), starting with pre-trained models and working back to theory. You use PyTorch and fast.ai's library to build real applications: computer vision, NLP, recommendation systems. The course is video-based with hands-on notebooks. After finishing, you can build working deep learning models without a PhD. The main limitation: this is top-down, not bottom-up. You learn to use models before understanding the math. For a pure theory foundation, pair this with Andrew Ng's Deep Learning Specialization.
fast.ai's courses have been taken by over 1 million learners worldwide, and their top-down approach has influenced how deep learning is taught globally (source: fast.ai community statistics).
About This Course
fast.ai's legendary course on deep learning for practitioners. Takes a top-down approach — you build real models in lesson 1 and understand the theory gradually. Covers CV, NLP, tabular data, and stable diffusion. Completely free.
Course Details
Frequently Asked Questions
Is fast.ai free?
Yes, completely free. Video, notebooks, and all materials are open.
Do I need deep learning experience?
No. This is for beginners, but assumes basic Python knowledge.
Should I learn math first?
No. fast.ai is designed so you learn to build first, then understand the math as you go.
Is fast.ai better than Andrew Ng's courses?
Different approaches. fast.ai is top-down and practical; Ng is bottom-up and theoretical. Take both for a complete picture.
Can I get a job with this?
Yes, if you build real projects and can explain them. The course alone isn't enough; you need portfolio work.
More generative-ai Courses
freeCodeCamp's comprehensive introduction to generative AI covering the OpenAI API, Gemini Pro, LangChain, RAG basics, and building real AI-powered apps. Completely free on YouTube.
Harvard's introduction to AI with Python. Covers search, knowledge representation, uncertainty, optimisation, machine learning, neural networks, and NLP.
Learn to build and deploy with transformers and large language models in this 12-chapter course from Hugging Face. You'll start with NLP fundamentals, then move into modern LLM techniques: tokenization, fine-tuning, reasoning models, and building shareable demos. By the end, you'll know how to use pre-trained models, customize them on your own data, and integrate them into real applications. Best if you have some Python experience; the course assumes basic familiarity with machine learning. Fair warning: LLM chapters move fast, so budget time for rereading.