Python is the language of PyTorch and of the agent frameworks and evaluation tools these courses teach, and both OpenAI and Anthropic publish an official SDK for it. That gives you a head start, not a result: the difference between a Python developer who has "tried the API" and one who ships AI systems in production shows in architecture, evaluation and operations, not in syntax.
Much of what is new in applied AI is available in Python: OpenAI and Anthropic publish official Python SDKs, the Model Context Protocol has an official Python SDK, and PyTorch, the Hugging Face libraries, inference servers such as vLLM and evaluation tools such as DeepEval and Ragas all live here. If you write Python, you do not have a foreign ecosystem to learn. You have to learn to use this one properly.
The risk for a Python developer is different from the one your Java or .NET colleagues face: not missing tools, but shallowness. A notebook with one API call and a long prompt is not an application. What sets you apart is being able to build a RAG pipeline you can evaluate, an agent with memory and guardrails, and a service with retries, cost under control and regression tests on answer quality.
A language model is a non-deterministic component with high latency and variable cost. Streaming, tool use, structured outputs, retries and token budgets are now part of everyday work, the way database connections used to be.
Embeddings, chunking, vector databases and retrieval strategies enter the usual stack. A Python developer who understands RAG can connect an internal source to a model and keep control over what the model "knows".
It is no longer enough for the function to return without an exception. You need golden datasets, metrics for retrieval and generation, a calibrated LLM-as-a-judge and regression tests in CI; otherwise every prompt change is an invisible regression.
Cursor, Claude Code and terminal agents work on Python codebases. Whoever knows how to give them context, limit their permissions and verify their output ships faster without losing control of the code.
Each of these changes has at least one course in the track below. See the track
When you have gone through all the lessons of a course and have an average of at least 70% on its quizzes, you issue from your account, after confirming the conditions, a Certificate of Completion with a unique number and a public verification link. It is not issued automatically and it comes at no extra cost, with a plan or with single-course access.
A private attestation of course completion. It is NOT a diploma or a state-recognised qualification under Romanian Laws 198/2023, 199/2023 or Government Ordinance 129/2000; employers may take it into account at their own discretion.
In the order in which they matter. If you already have experience with model APIs, skip the foundations. For each course, the reason it matters to someone who works with Python.
You start with what you already know, Python, and add the mental model of AI systems: how an LLM works, what it can and cannot do, and how you call it properly from code.
The map of the field, from LLMs and embeddings to agents and evaluation, with its code examples mostly in Python, so you know which part of the stack you want to specialise in.
Key modules: Machine Learning Essentials · Large Language Models and Modern Architectures · Working with LLM APIs and Integrations · AI Tools for Developers
Read the free preview Course details This course only: €99/monthThe modules on prompt engineering for code and for agents show how to write instructions that produce verifiable code and decisions, not just pleasant text.
Key modules: Advanced Prompting Techniques · Multi-Modal Prompt Engineering · Prompt Engineering for AI Agents · Prompt Engineering for Code
Read the free preview Course details This course only: €99/monthStreaming, error handling, function calling, multi-model routing and productionization, with the examples mostly in Python: the part notebooks skip and production demands.
Key modules: Streaming and Error Handling · Function Calling and Tool Use · Model Context Protocol (MCP) · Multi-Model Orchestration and Optimization
Read the free preview Course details This course only: €99/monthThis is where the difference is made: you turn API calls into systems with your own data, memory, tools and predictable behaviour.
Chunking, embeddings, hybrid search, re-ranking and the systematic evaluation of a RAG pipeline, built with the Python frameworks the field uses: LangChain, LlamaIndex and Haystack.
Key modules: Implementing the RAG Pipeline · Embedding Models and Document Processing · Advanced RAG · Advanced RAG Architectures
Read the free preview Course details This course only: €99/monthAgents with memory, state and tools, design patterns for production, guardrails and observability, built with frameworks and SDKs from the Python ecosystem such as LangGraph, the OpenAI Agents SDK and CrewAI.
Key modules: Memory, State and Advanced Reasoning · Frameworks and SDKs for AI Agents · Model Context Protocol (MCP) and Interoperability · Workflow Automation with AI
Read the free preview Course details This course only: €99/monthA whole module on building MCP servers with Python, using FastMCP: your tools become available to any compatible client.
Key modules: Building MCP Servers with Python · Building MCP Servers with TypeScript · Transport, Authentication and Configuration · Enterprise Patterns and Production Architectures
Read the free preview Course details This course only: €99/monthMemory types, retrieval for context and compaction, with the examples in Python: what you do when the context window is no longer enough and the agent "forgets".
Key modules: The Anatomy of Context: The Components of the Inference Window · Memory Types for AI Agents · Memory Managers and Persistence: Extraction, Consolidation, Store · Retrieval Strategies for Context: Vector, Graph, Relational, Hybrid
Read the free preview Course details This course only: €99/monthAn AI system ships with metrics and has to be operated. This is the stage where you answer for quality, for the models you train yourself and for how they run.
promptfoo, DeepEval, Ragas and pytest in practice: golden datasets, a calibrated LLM-as-a-judge and regression tests in CI/CD, so you stop deploying quality regressions.
Key modules: Classic Metrics and Why They Fail on Open-Ended Text · LLM-as-a-Judge: Design, Bias and Calibration · Building Evaluation Datasets · The Evaluation Tooling Landscape
Read the free preview Course details This course only: €99/monthBuilt entirely on PyTorch: tensors, autograd, training loops, CNNs and Transformers, GPU training and ONNX export. The layer under the APIs, in your language.
Key modules: PyTorch Tensors and Autograd · From Linear Regression to Neural Networks · Backpropagation and Gradient Descent · Loss Functions and Optimizers
Read the free preview Course details This course only: €99/monthFor when prompting and RAG are not enough: datasets, LoRA and QLoRA with the Hugging Face toolchain, evaluation, quantization and self-hosting with Ollama and vLLM.
Key modules: Data: The Foundation of Fine-Tuning · Parameter-Efficient Fine-Tuning (PEFT): LoRA · QLoRA and Quantized Training · The Fine-Tuning Toolchain
Read the free preview Course details This course only: €99/monthExperiment tracking, training pipelines, a model registry, serving and drift monitoring: the lifecycle around the models you train, for when your project has trained models and not only API calls.
Key modules: Experiment Tracking and Reproducibility · Data and Feature Management · Training Pipelines and Orchestration · Model Registry, Versioning, and CI/CD for ML
Read the free preview Course details This course only: €99/monthPipelines, data quality, embeddings at scale and vector databases, with Python and SQL throughout: the data layer every retrieval system depends on.
Key modules: Storage and Table Formats: Parquet and Iceberg · Ingestion from Diverse Sources · ETL/ELT and Orchestration · Data Quality, Validation, and Contracts
Read the free preview Course details This course only: €99/monthAgentic coding from the terminal on real codebases: plan, edit, review, deterministic hooks, CI and least-privilege permissions, for the Python projects you already maintain.
Key modules: Multi-File and Large Codebases: Plan, Edit, Review and Context Management · The Full Git Workflow: Branches, Commits, Conflicts, Code Review and PRs · Headless Mode (claude -p) and Non-Interactive Automation · Subagents and Agent Teams: Orchestration, Delegation and Controlled Parallelization
Read the free preview Course details This course only: €99/monthThe course in the track that ends with a capstone project. The list comes from the real modules of the courses, not from a brochure.
What already exists, so that you do not start from zero. The courses in the track teach you what to build with it.
These are the skills you practise in the courses of the track; results depend on the time you invest and on your previous experience.
Start the Python trackCode examples, architectures and case studies, and in some courses a capstone project at the end. Not summaries of documentation, but structured material that you go through in order, at your own pace.
You ask in the context of the lesson, by typing or by voice, and get an explanation of the concept you are on. Every lesson has its quiz: about 355 questions per course on average. The professor is an AI system, not a human teacher, included in every plan with a fair-use limit.
Agentic coding, MCP, RAG, agents, evals, LLM security, fine-tuning, AIOps, computer vision and Edge AI are whole courses in the catalog, not closing chapters.
A preview of the first lesson of every course can be read without an account. Then you choose a subscription by number of courses, from €99 a month for one course to €319 a month for 10 courses, VAT included; you pick the courses and can change them at every renewal. You can stop the renewal anytime from your account; access stays until the end of the period already paid for.
You pick them, with the AI Professor included, and can change them at every renewal. The whole track fits in a subscription of 15 courses (€359 a month, VAT included); with the full catalog (€499) you have every course.
Pay for the tier, then pick the courses in your account. You can change them at every renewal.
€99 a course
€99a month €990a year, 12 months of access
under €67 a course
€199a month €1,990a year, 12 months of access
under €52 a course
€259a month €2,590a year, 12 months of access
under €32 a course
€319a month €3,190a year, 12 months of access
under €24 a course
€359a month €3,590a year, 12 months of access
under €16 a course
€399a month €3,990a year, 12 months of access
under €10 a course, across all 50
€499a month €4,990a year, 12 months of access
Total prices, VAT included. Move up anytime and pay only the difference for the days left; move down at renewal. Renews automatically, you stop it anytime from your account; your progress and certificates stay. Digital content with immediate access: see the withdrawal terms.
A subscription by number of courses: 1, 3, 5, 10, 15 or 25 courses a month, or the full catalog. You pick them and can change them at every renewal. Compare all the subscriptions · The team library, priced per member · Give a month of access as a gift
The same skills, seen from each ecosystem. The languages close to Python are highlighted.
Most of them use Python for their code examples: the introduction, LLM integration, RAG, agents, context engineering, evaluation, PyTorch, fine-tuning and data engineering. The MCP course has one module on servers in Python and one on servers in TypeScript. A few courses, such as MLOps, AI security and the AI SaaS course, explain architecture and practice in prose rather than through code listings.
Not for most applications. Integrating APIs, RAG, agents and evaluation are software engineering, not research. The introduction to AI engineering has a module on the machine learning essentials you need to make informed decisions. PyTorch, fine-tuning and MLOps are optional steps, for when the project needs its own models.
Skip the foundations stage and start with RAG or with advanced LLM integration, then continue with agents and evaluation. Every course has a free preview of its first lesson, readable without an account, so you can check the level before you subscribe.
All the courses on this page are IT courses. You pick them in a subscription by number of courses: usually 3, 5 or 10 courses a month for one language and stack; there are also 15 and 25 courses, a single course and the full catalog. You pick the courses and can change them at every renewal. The subscription renews automatically; you can stop the renewal anytime from your account, and access stays until the end of the period already paid for. The prices, with VAT included, are shown below and on the pricing page.
13 courses, ~327 hours, all in a subscription of 15 courses: €359 per month, VAT included. You can change them at every renewal; the AI Professor is built into every lesson.
The courses are for educational purposes only: they are not professional advice, and completing them is not a diploma or a state-recognised qualification. The names of languages, tools and products belong to their owners and are mentioned for identification only. Details in the Terms & Conditions.
ChatGPT, OpenAI, Claude, Anthropic, Gemini, Google, Microsoft 365 Copilot, Cursor, Midjourney and the other product and company names mentioned are trademarks or trade names of their respective owners. Cursuri-AI.ro is an independent training provider and is not affiliated with, sponsored by or endorsed by any of them.