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AI (artificial intelligence) thumbnail

AI (artificial intelligence)

314 courses 15 categories

Part of Learn Data & AI

AI and machine learning as a topic spans everything from classical statistical learning to the foundation models reshaping how software is built. The category covers two distinct skill tracks. The first is applied AI engineering — integrating language models, building RAG pipelines, designing agents, and shipping AI features in production. The second is traditional machine learning — supervised learning, neural networks from scratch, the math underneath modern systems.

The frontier moved fast through 2024-2025. Closed-model providers (OpenAI, Anthropic, Google, xAI) compete on benchmarks. Open-weight models (Llama, Qwen, Mistral, DeepSeek) reached parity for most tasks at a fraction of the cost when self-hosted. Toolchains stabilized: PyTorch dominates research, vLLM and llama.cpp run inference, Hugging Face hosts the ecosystem, and a new layer of agentic frameworks (LangGraph, OpenAI Agents SDK, CrewAI) handles orchestration.

What you'll find under this topic

  • Large language models: transformers, fine-tuning, RLHF, DPO, model evaluation
  • Applied AI: ChatGPT and Claude integration, prompt engineering, AI agents
  • Retrieval-Augmented Generation: chunking, embeddings, vector stores (pgvector, Pinecone, Qdrant), reranking
  • Computer vision: OpenCV, image generation (Stable Diffusion, FLUX), object detection
  • Math foundations: linear algebra, probability, calculus for ML
  • Production AI: cost control, evaluation, prompt-injection defense, observability

The roles hiring against this topic include ML engineers at companies like OpenAI, Anthropic, Google DeepMind, and Meta AI; AI product engineers at any SaaS company adding LLM features; and applied scientists at Spotify, Netflix, and Uber where recommendation systems still drive significant revenue.

Top 10 picks for 2026

Categories (15)

AI Agents thumbnail
AI Agents
AI agents are autonomous loops where a language model decides which tool or function to call next, runs it, observes…
AI App Building thumbnail
AI App Building
AI app building covers the work of turning an LLM API into a product that real users pay for. The category sits between…
AI for Business & Product thumbnail
AI for Business & Product
AI for Business & Product focuses on the practical integration of artificial intelligence into existing business…
AI-Assisted Coding thumbnail
AI-Assisted Coding
AI-assisted coding is the workflow built around large language models that write, refactor, review, and explain code…
ChatGPT thumbnail
ChatGPT
ChatGPT is OpenAI's conversational interface to its GPT family of models, launched in November 2022. The category…
Claude Code thumbnail
Claude Code
Claude Code is a tool developed by Anthropic that leverages the capabilities of the Claude model to enhance the…
Data processing and analysis thumbnail
Data processing and analysis
Data processing and analysis covers the day-to-day work of turning raw operational data into something a person or…
LLMs & Fundamentals thumbnail
LLMs & Fundamentals
LLMs (large language models) are neural networks trained on enormous text corpora to predict the next token given a…
Machine learning thumbnail
Machine learning
Machine learning is a subset of artificial intelligence that enables computers to learn from data and make decisions…
Math & Statistics thumbnail
Math & Statistics
Math & Statistics underpin many aspects of software engineering and data science, providing the foundational tools…
Model Context Protocol (MCP) thumbnail
Model Context Protocol (MCP)
Model Context Protocol (MCP) is an open standard introduced by Anthropic in late 2024 to streamline the integration of…
Other (AI) thumbnail
Other (AI)
Other (AI) encompasses a diverse range of AI-adjacent technologies that extend beyond the conventional boundaries of…
Prompt Engineering thumbnail
Prompt Engineering
Prompt Engineering is the discipline of crafting precise instructions for language models to ensure consistently…
Python thumbnail
Python
Python is a high-level, general-purpose programming language designed around code readability and a deliberately small…
RAG (Retrieval-Augmented Generation) thumbnail
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is an innovative architectural pattern that enhances the capabilities of language…

Courses (314)

Showing 1 – 30 of 314 courses

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Frequently asked questions

Is AI a good career path in 2026?
Yes. Demand still outpaces supply across applied AI engineering, ML research, and AI product roles, and pay sits at the top of the engineering market. The biggest hiring shift since 2024 is that 'AI engineer' now usually means LLM-integration and agent work rather than training models from scratch, so a software-engineer background plus solid Python is enough to enter without a PhD.
Do I need a math or PhD background to work in AI?
Not for applied AI — building products with foundation models, RAG pipelines, agents, and evaluation only needs working Python and ML literacy. Deep math (linear algebra, probability, optimization) is required for ML research, model architecture work, and fine-tuning at scale. Most production AI engineering jobs sit firmly in the applied bucket.
AI vs Machine Learning — which should I learn first?
Start with applied AI (LLM APIs, prompts, RAG) if your goal is shipping features fast; classical ML and the math underneath make more sense if you want depth in model behaviour, fine-tuning, or research. Most engineers today learn applied AI first and pick up ML fundamentals as needed when LLM outputs need debugging or evaluation.
What stack do AI engineers actually use day to day?
Python is the default language. Inference talks to OpenAI, Anthropic, or open-weight models via vLLM or llama.cpp. Orchestration leans on LangGraph, OpenAI Agents SDK, or hand-rolled state machines. Storage uses pgvector, Qdrant, or Pinecone for retrieval. Evaluation runs on LangSmith, Braintrust, or in-house harnesses. PyTorch shows up for any custom training.
How long until I can ship something real with AI?
A working RAG prototype or simple agent is a weekend if you already write code — the APIs are well-documented and the toolchain is mature. Reaching production quality (latency budgets, eval harness, cost control, prompt-injection defense) is typically 2–4 months of consistent project work. Hireable depth in applied AI takes 6–12 months.