Hugging Face LLM Course

Transformers is Hugging Face's open-source model-definition framework and Python library for inference and training with pretrained text, computer vision, audio, video, and multimodal machine-learning models. It centralizes model definitions behind a unified API and provides the Pipeline interface, which preprocesses inputs and returns task-specific outputs for applications such as text generation, automatic speech recognition, image classification, and visual question answering. Models can be downloaded and cached from the Hugging Face Hub, and the library supports moving models between PyTorch, JAX, and TensorFlow workflows while exposing model internals for customization. It requires Python 3.10 or later and PyTorch 2.5 or later for the documented installation.

Hugging Face is an AI and machine-learning collaboration platform that hosts and distributes models, datasets, and applications. Its Hub supports public model, dataset, and application repositories across text, image, video, audio, and 3D workloads, while its open-source stack includes Transformers, Diffusers, Safetensors, Tokenizers, TRL, Transformers.js, smolagents, and PEFT. The platform provides a unified API for accessing models from AI providers, GPU-based compute and Inference Endpoints, and paid team and enterprise features such as private datasets, access controls, single sign-on, audit logs, and dedicated support.

DeepLearning.AI courses is an online catalog of AI education from DeepLearning.AI, covering prompting, retrieval-augmented generation, agents, generative models, LLM operations, search and retrieval, AI coding, and related subjects. The catalog is organized into short courses, broader courses, and professional certificates with beginner and intermediate levels, topic filters, and collaborator listings. Featured courses include AI Prompting for Everyone, Build with Andrew, and Agentic AI; the video notes that many courses are free and can be audited.

AI Engineering Lab is a free, self-paced 24-week AI engineering course developed by Zorost Intelligence AI Lab. It takes learners from Python and machine learning through deep learning, LLM internals, prompt and context engineering, retrieval-augmented generation with vector search, quantization, LoRA and DPO fine-tuning, evaluation harnesses, coding-agent harnesses, AI agents, MCP, and cloud and Databricks platforms including Azure AI Foundry, Google Vertex AI, AWS Bedrock, and Databricks. The curriculum uses one continuous fictional freight-company case study. Each week pairs a concept with a runnable Python, SQL, or PySpark artifact; from Week 3 onward, the work includes a metric and an error note. The repository contains 43 runnable notebooks, publishes the full curriculum online, requires no signup or paid API key, and states that the first eight weeks require no GPU. It is MIT licensed.

Machine Learning Crash Course is a free, relatively short online course from Google for Developers covering core machine-learning concepts through interactive lessons. It covers linear and logistic regression, classification, numerical and categorical data, datasets and overfitting, neural networks, embeddings, large language models, production machine-learning systems, AutoML, and model fairness.