AI (artificial intelligence)
337 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 (337)
Showing 1 – 30 of 337 courses
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 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
NewStudy 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 23m
NewMaster machine learning with Hugging Face. A practical course from basics to real-world projects. Minimum theory, maximum practice.23h 23m
NewEnhance your qualifications in machine learning by learning about infrastructure, deployment, and full lifecycle management of ML in 8 weeks. Become in demand.14h 2m
NewMaster agent architecture and create an AI application in 6 weeks. Become an indispensable orchestrator of intelligent agents and boost your career.7h 6m
NewA series of videos on techniques and processes for quick application. Learn how to automate routine tasks and improve development with the help of AI.45m
NewLearn how to use a system of rules to maintain high code quality by applying directives and recommendations in a project using Claude Code.53m
NewMaster 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
NewDiscover a rich set of custom skills for Claude Code. Simplify development and auditing with ready-made AI tools and commands.
NewLearn how to create an npm package for CLI Counselors using TypeScript and Node.js. A complete practical course with testing and automation through GitHub Actio38m
NewExplore the creation of the SOLO application: manage development in one place. A unified interface for npm, composer, and servers, with support for multiple pro2h 3m
NewLearn code audit skills to analyze and improve the codebase. The course covers tools for systematic analysis, project cleanup, and working with reports.30m
Updated 1mo agoTake the course and create a desktop application called Loadout to manage AI tools using modern technologies such as Rust and React.11m
Updated 1mo agoMaster the development of the AI-native platform faster.dev using modern technologies, including Laravel and React, to create efficient SaaS products.22m
Updated 1mo agoStudy the universal principles of system design for AI applications and platforms. Apply the knowledge to develop complex digital systems and manage them.4h 22m
Updated 1mo agoPractical Guide to Mastering Claude Code. Learn the basics and key features step by step to confidently use the tool.1h 7m
Updated 1mo agoStudy Codex from the basics to advanced techniques. The course will help you use it as an intelligent assistant, enhancing your skills and increasing productivi3h 10m
Updated 1mo agoLearn how to create robust automations with n8n and AI. This includes AI agents, email processing, content generation, and image generation.
Updated 1mo agoStart learning Python from scratch: set up the environment, learn the basics, and gain confident programming skills for your own projects.2h 40m
Updated 1mo agoStudy AI reasoning models from scratch. Learn how they work, are trained, and applied by exploring real-world behavior analysis and reasoning steps.4h 37m
Updated 1mo agoPractical course on AI development for engineers. Learn reproducible processes and improve your code with artificial intelligence.5/5
Updated 1mo agoLearn to fully utilize the capabilities of Claude Code. Turn knowledge into effective skills and boost your productivity in software development.2h 46m
Updated 1mo agoMaster Claude Code by acquiring practical knowledge and techniques necessary for working with the terminal and automation. A structured program will accelerate8h 21m5/5
Updated 1mo agoStudy the creation of voice AI agents using AWS and Python. Develop an assistant with real functionalities and a deep understanding of the architecture.3h 1m5/5
Updated 1mo agoMaster the creation of AI applications for investments using Python and LangChain. Practice developing a fintech application and understanding financial metrics7h 36m5/5
Updated 1mo agoLearn core regression models and use them in Python. You study linear, logistic, log, and Cox models with clear steps and real data.6h 20m
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.