Learning Resources for Every Level

Andrew Ng's non-technical intro to AI for business leaders and curious minds. Learn what AI can and cannot do, how to navigate AI strategy, and ethical considerations.

The definitive ML course from Andrew Ng updated for 2023. Covers supervised learning, unsupervised learning, and practical best practices for building ML systems.

Top-down, code-first approach to deep learning using PyTorch and fastai. Highly recommended for developers who want to build real-world deep learning applications quickly.

Beautiful visual explainer of neural networks, backpropagation, and gradient descent. The best mathematical intuition builder available online. Four video series.

Build neural networks from scratch in Python, progressing from micrograd to implementing GPT-2. Demystifies backpropagation, tokenization, and transformer architecture.

Foundation-level AI/ML certification from AWS. Validates understanding of AI concepts, AWS AI services (SageMaker, Rekognition, Comprehend), and responsible AI practices.

Professional certification for ML engineers working on Google Cloud Platform. Covers ML problem framing, data preparation, model development, and MLOps on Vertex AI.

Comprehensive guide covering all major prompt engineering techniques: zero-shot, few-shot, chain-of-thought, tree-of-thought, and advanced prompting strategies.

Hands-on NLP with the Transformers library. Learn to fine-tune BERT, GPT-2, and other models. Covers tokenization, training, evaluation, and model deployment.

Free online course from the University of Helsinki introducing AI concepts to non-technical audiences. Over 1 million students enrolled worldwide.

Stanford's flagship ML course with full lecture videos online. Covers linear algebra foundations, supervised learning, unsupervised learning, and theoretical ML concepts.

Jay Alammar's visual and intuitive explanation of the Transformer architecture. The best resource for understanding attention mechanisms, positional encoding, and encoder-decoder structure.

Entry-level Azure AI certification. Covers AI workloads, machine learning concepts, computer vision, NLP, and responsible AI principles on Azure.

5-course series covering neural networks, hyperparameter tuning, regularization, CNNs, RNNs, and sequence models. By Andrew Ng — the definitive deep learning curriculum.

An 8-week course on AI alignment and safety research. Covers value alignment, robustness, interpretability, and governance for those interested in AI safety careers.

Cohere's free curriculum for learning large language models from basics to advanced RAG, fine-tuning, and deployment. Includes hands-on labs with the Cohere API.

MIT's intensive deep learning short course. Covers foundations, CNNs, RNNs, GANs, reinforcement learning, and research frontiers. Lecture videos freely available.

4-course specialization on deploying ML systems in production. Covers concept/data drift, CI/CD for ML, model monitoring, and ML system design by Andrew Ng.

Short hands-on course teaching LangChain fundamentals: chains, memory, agents, and document Q&A. Co-taught by LangChain founder Harrison Chase.

Google's official generative AI learning path covering LLMs, image generation, responsible AI, and building generative AI applications with Google Cloud tools.