AI (artificial intelligence)
353 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.
Categories (15)
AI agents are autonomous loops where a language model decides which tool or function to call next, runs it, observes…
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 focuses on the practical integration of artificial intelligence into existing business…
AI-assisted coding is the workflow built around large language models that write, refactor, review, and explain code…
ChatGPT is OpenAI's conversational interface to its GPT family of models, launched in November 2022. The category…
Claude Code is a tool developed by Anthropic that leverages the capabilities of the Claude model to enhance the…
Data processing and analysis covers the day-to-day work of turning raw operational data into something a person or…
LLMs (large language models) are neural networks trained on enormous text corpora to predict the next token given a…
Machine learning is a subset of artificial intelligence that enables computers to learn from data and make decisions…
Math & Statistics underpin many aspects of software engineering and data science, providing the foundational tools…
Model Context Protocol (MCP) is an open standard introduced by Anthropic in late 2024 to streamline the integration of…
Other (AI) encompasses a diverse range of AI-adjacent technologies that extend beyond the conventional boundaries of…
Prompt Engineering is the discipline of crafting precise instructions for language models to ensure consistently…
Python is a high-level, general-purpose programming language designed around code readability and a deliberately small…
RAG (Retrieval-Augmented Generation) is an innovative architectural pattern that enhances the capabilities of language…
Courses (353)
Showing 1 – 30 of 353 courses
NewPractical course on mastering Claude Cowork. Learn to use AI agents, create workflows, connect services, and automate tasks.2h 18m5/5
NewThe intensive course on developing practical AI skills in 30 days helps expedite the creation of texts, research, and sustainable workflows.6h 52m5/5
NewA practical course on AI in ethical hacking and cybersecurity will help you master automation analysis, SOC enhancement, and modern protection methods.8h 18m5/5
NewPractical course on integrating Claude Code into DevOps processes: installation on Linux, security, automation, and creating a real project portfolio.2h 30m
NewIntensive course on modern computer vision models with a focus on practice, segmentation, detection, and deployment of solutions.6h 8m
NewLearn to integrate Claude Code into your workflow. Create an AI application from scratch and secure it using modern technologies and practices.8h 57m5/5
NewLearn to properly formulate AI tasks to increase efficiency and prevent project failures. Includes a 5-step framework and access to 200+ case studies.7h 3m5/5
NewLearn how to create applications with Next.js: UI design, authentication, databases, UX, payments via Stripe, and email automation. Suitable for beginners.8h
NewMaster the practical implementation of AI models in applications using cloud services. Learn to work with APIs, scalable AI services, and create prototypes.27m
NewJoin the masterclass on Full Stack AI Development. Learn about AI system architecture and creation from scratch, integrations, and engineering practices.2h 36m5/5
NewBuild a successful career in AI: from Junior to Senior. Learn popular strategies, create a personal brand, and acquire valuable skills for growth.5h 2m
NewMaster modern AI with the Advanced Local AI course. Learn to use and integrate open-source models for real-world tasks.1h 1m
NewGain a practical understanding of AI product development: from MVP to a full-fledged solution, process automation, and testing of AI applications.3h 10m
NewLearn how to create a full-stack application using Next.js and the AI tool Claude Code. The entire development cycle from idea to production, including UX.5h 1m
NewLearn how to create a complete ML solution from data to cloud deployment. Master the end-to-end pipeline and professional code architecture.2h 55m
NewGain practical skills in AI system development based on professional experience. Master the tools and approaches for successful AI solution implementation.1h 49m
NewLearn to create AI applications using TypeScript and Python, with a focus on practice and using AI tools. Gain skills for development.1h 59m
NewStudy AI programming techniques for your projects. The course reveals strategies and approaches for the effective use of AI tools. Author: Zen van Riel.2h 51m5/5
NewPractical training in modern AI technologies. Learn LLM, create a question-answer service, and acquire a knowledge base on AI.1h 34m
NewStudy the Solveit method for solving tasks using code. The course covers algorithms, web development, system administration, and startup creation.23h 24m
NewStudy modern practices with generative AI and become an AI engineer. Apply Claude Code to real-world tasks to create reliable solutions.9h 31m5/5
NewTake a 26-hour course on AI project development in Golang. Create 6 advanced projects, enhancing your skills in building scalable solutions.25h 50m0/5
Updated 1mo agoStudy the systematic approach to development with AI. Master the AI workflow, work with MCP servers, and create the DevStash platform. The course takes you from16h 23m5/5
Updated 1mo agoMaster machine learning with Hugging Face. A practical course from basics to real-world projects. Minimum theory, maximum practice.23h 23m
Updated 1mo agoEnhance your qualifications in machine learning by learning about infrastructure, deployment, and full lifecycle management of ML in 8 weeks. Become in demand.14h 2m
Updated 1mo agoMaster agent architecture and create an AI application in 6 weeks. Become an indispensable orchestrator of intelligent agents and boost your career.7h 6m
Updated 1mo agoLearn how to use a system of rules to maintain high code quality by applying directives and recommendations in a project using Claude Code.53m
Updated 1mo agoMaster the skills to adapt language models to your tasks. Learn how to create effective skills and avoid the risks of using off-the-shelf solutions.1h 51m
Updated 1mo agoDiscover a rich set of custom skills for Claude Code. Simplify development and auditing with ready-made AI tools and commands.
Related topics
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.