Develop AI

Develop AI is for building AI systems in code. This track lists hands-on courses for developers and data scientists: building on large language models (LLMs) through their application programming interfaces (APIs), building agents, fine-tuning models, and running models in production with machine learning operations (MLOps). It also explains the core concepts behind them.

Featured courses

These are our picks: hands-on courses to build from.

Elements of AI: Building AI

University of Helsinki & MinnaLearn

FreeIntermediate

Claude Code 101

Anthropic

FreeBeginner

Developing LLM Applications with LangChain

DataCamp

PaidIntermediate

Professional Machine Learning Engineer Certification

Google Skills

Advanced

Introduction to Deep Learning with PyTorch

DataCamp

PaidIntermediate

Operationalize machine learning models (MLOps)

Microsoft Learn

FreeIntermediate

Browse Develop AI by area

Each area covers one part of the track: a subject, a skill, or a kind of work. To browse the whole track instead, open the first card and filter the list yourself.

All Develop AI courses

Browse the whole Develop AI track in the course catalog, then narrow by cost, level or provider.

AI Infrastructure & MLOps

Deploy, scale, and monitor AI in production — MLOps and infrastructure for engineers.

Building AI Agents

Build working AI agents with tools, memory, and orchestration — for developers.

Deep Learning

Build and train neural networks — the models behind modern AI, hands-on in code.

LLM & Language AI Development

Build with large language models — retrieval, semantic search, and training your own.

Machine Learning Engineering

Build, train, and ship machine learning models — hands-on courses for developers.

Key concepts

Each of these glossary terms relates to at least one course on this track, from core ideas to named products such as ChatGPT. Start here if you want a quick, plain-English definition first.

Agentic AI AI Agent AI Coding Assistant AI Copilot AI Ethics AI Governance Artificial Intelligence (AI)Attention Mechanism Autonomous Agent Backpropagation Bias in AI ChatGPT Classification Claude Clustering Code Generation Computer Vision (CV)Context Engineering Context Window Convolutional Neural Network (CNN)Data Augmentation Data Labeling Dataset Deep Learning Diffusion Model Dropout Embeddings Feature Engineering Feature Extraction Fine-Tuning Foundation Model Function Calling Gemini Generative Adversarial Network (GAN)Generative AI Gradient Descent Graphics Processing Unit (GPU)Guardrails Hallucination Human in the Loop Hyperparameter Image Segmentation Inference Knowledge Distillation Knowledge Graph Large Language Model (LLM)LLMOps (Large Language Model Operations)Machine Learning Machine Translation MLOps (Machine Learning Operations)Model Context Protocol (MCP)Model Deployment Model Drift Model Monitoring Multi-Agent System Multimodal AI Named Entity Recognition (NER)Natural Language Processing (NLP)Natural Language Understanding (NLU)Neural Network Object Detection Optical Character Recognition (OCR)Orchestration Overfitting Pretraining Prompt Engineering Pruning Quantization Regression Regularization Reinforcement Learning from Human Feedback (RLHF)Responsible AI Retrieval-Augmented Generation (RAG)Semantic Search Small Language Model (SLM)Speech Recognition Speech-to-Text (STT)Supervised Learning Synthetic Data Tensor Processing Unit (TPU)Text-to-Image Text-to-Speech (TTS)Text-to-Video Tool Use Training Data Transfer Learning Transformer Vector Database Vision-Language Model (VLM)Zero-Shot Learning

Browse the full AI glossary