AI (artificial intelligence)
365 courses15 categories
Part of LearnData & 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…
Computer vision is a dynamic field of artificial intelligence focused on enabling machines to interpret and make…
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…
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(365)
Showing 1–30 of 365 courses
NewThe practical course on AI development helps engineers implement an AI-first approach, accelerate code creation, and improve development quality.12h 48m
NewClaudify transforms the terminal into a powerful AI environment with memory, agents, and checks, providing stable and efficient development with Claude Code.5/5
NewPractical course on creating AI applications on AWS Bedrock. Learn about generative AI, working with LLM, model tuning, and deploying scalable solutions.15h 17m
NewThe course helps to find early market insights using AI research, analyze real data, and create your own decision-making system.6h 34m5/5
NewThe course on data engineering patterns helps to master architectural approaches and build reliable pipelines to enhance data engineering.13h 15m
NewPractical course on Apache Spark for confident work with distributed data processing, pipeline optimization, and creating efficient Big Data solutions.4h 28m
NewThe Go and AI Agent Intensive course will teach you how to work with specifications, context engineering, and agent workflows for confident code development8h 16m
NewThe course teaches how to implement AI in production, manage context, design architecture, and create reliable agents to accelerate development.10h5/5
Updated 1mo agoPractical course on creating AI workflows and agents in Microsoft Foundry and Azure AI. Master architecture, integrations, and development of production4h 38m
Updated 1mo agoPractical course on Claude Code will help you master the basics and advanced techniques, create AI workflows, and accelerate development in real projects.11h 33m5/5
Updated 1mo agoPractical course on creating AI systems and working with LLM. You will master prompting, agents, and RAG, and create prototypes and production solutions.20h 52m5/5
Updated 1mo agoThe practical course on working with Claude Code helps quickly master the CLI agent, context engineering, and the creation of resilient multi-agent systems8h 44m5/5
Updated 1mo agoMaster AI tool development and create your own CLI by studying agent system architecture and principles of working with AI in real-world tasks.34h 18m
Updated 2mo agoPractical course on mastering Claude Cowork. Learn to use AI agents, create workflows, connect services, and automate tasks.2h 18m5/5
Updated 2mo agoThe intensive course on developing practical AI skills in 30 days helps expedite the creation of texts, research, and sustainable workflows.6h 52m5/5
Updated 2mo agoThe Apache Flink course helps master stream data processing, deploy and optimize real-time pipelines for professional projects.2h 12m
Updated 2mo agoPractical course on integrating Claude Code into DevOps processes: installation on Linux, security, automation, and creating a real project portfolio.2h 30m
Updated 2mo agoIntensive course on modern computer vision models with a focus on practice, segmentation, detection, and deployment of solutions.6h 8m
Updated 3mo agoLearn to integrate Claude Code into your workflow. Create an AI application from scratch and secure it using modern technologies and practices.8h 57m5/5
Updated 3mo agoLearn 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
Updated 3mo agoLearn how to create applications with Next.js: UI design, authentication, databases, UX, payments via Stripe, and email automation. Suitable for beginners.8h
Updated 3mo agoMaster the practical implementation of AI models in applications using cloud services. Learn to work with APIs, scalable AI services, and create prototypes.27m
Updated 3mo agoJoin the masterclass on Full Stack AI Development. Learn about AI system architecture and creation from scratch, integrations, and engineering practices.2h 36m5/5
Updated 3mo agoMaster modern AI with the Advanced Local AI course. Learn to use and integrate open-source models for real-world tasks.1h 1m
Updated 3mo agoExplore creating AI agents in Python without complex frameworks. Maintain full control over system logic and security, and work directly with AI APIs.2h 33m5/5
Updated 3mo agoGain a practical understanding of AI product development: from MVP to a full-fledged solution, process automation, and testing of AI applications.3h 10m
Updated 3mo agoLearn 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 1m5/5
Updated 3mo agoLearn how to create a complete ML solution from data to cloud deployment. Master the end-to-end pipeline and professional code architecture.2h 55m5/5
Updated 3mo agoGain practical skills in AI system development based on professional experience. Master the tools and approaches for successful AI solution implementation.1h 49m
Updated 3mo agoLearn to create AI applications using TypeScript and Python, with a focus on practice and using AI tools. Gain skills for development.1h 59m
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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.