The course is suitable for anyone who already knows the basics of programming and wants to get into the secrets of artificial intelligence. Previous experience with AI or machine learning is not required.
The course is ideal for anyone who wants to understand the principles of artificial intelligence through practical examples in Python.
You have development or testing experience and want to add machine learning, neural networks and specific AI tasks in Python to your technical practice.
You work with data and want to take the next step: use models for predictions, classification, text processing or image recognition.
You enjoy programming, try new technologies and want to see in your own code how AI models are built, trained and used.
You need to understand what AI can really do, where its limits are and how to make decisions about using it without relying only on marketing promises.
Teaching combines explanation, practical demonstrations, short exercises and independent work. First you understand a new topic, then you practise it on specific tasks – in either the weekday or the weekend format.
Explanation of the topic, live demonstrations in Python and short ongoing exercises – whether in an evening lesson or a weekend morning block.
Independent work on tasks, practising procedures and room for questions – in the weekday format in the practical lesson, at weekends in the afternoon blocks.
Video recordings of the lessons are available for you to return to when revising or catching up on missed material.
Between lessons (or between weekends) you receive assignments for independent practice so you can try the material outside class as well.
During the course you have access to a shared messenger for questions, sharing solutions and communicating with the lecturer and other participants.
Every new topic has a practical part: first the principle and typical use, then practice on tasks.
We are writing our own textbook for the course. Selected chapters – from machine learning and decision trees through convolutional and generative networks to natural-language processing – are available as free PDF downloads (in Czech). Get a picture of how we explain the material before the first lesson.
We offer the course in two formats – the specific days and times are always shown on the card of the chosen date.
In this block you get to know the basics of machine learning – from Bayesian statistics through decision trees to your first multilayer neural networks. You program every model yourself and try it on real data.
We learn how computers can predict and make decisions without pre-defined instructions. We explore the simple but powerful naive Bayes classifier, which classifies data by probability.
We enter the world of decision trees, which help make decisions based on data. We learn how to optimise model parameters and evaluate their results.
We go through the principle of multilayer neural networks and deep learning. We show how neural networks process inputs, learn from data and solve more complex tasks.
The second block focuses on convolutional and generative neural networks. You learn to recognise images, improve models with data augmentation and create generative outputs.
Convolutional neural networks are used to process image data. On a practical model we explain the principle of image filters and show an application that recognises objects in a picture.
We focus on data-augmentation techniques that help improve the results of image-recognition models. We try preparing data so that the model copes better with different variants of input.
We look at generative architectures and the principle of generative adversarial networks (GANs). We show how models can be used to create new image outputs.
We get to know autoencoders, neural networks that learn a simplified representation of input data. We show how autoencoders can be used to compress data, remove noise or lay the foundation for generative models.
The third block extends the scope to further types of tasks: finding patterns in data without supervision, time-series prediction and natural-language processing. At the end we focus on practice – preparing data before training and deploying and monitoring a model in production.
We get to know unsupervised learning, where the model looks for structures and patterns in data without prepared answers. We show what this approach is useful for in practice.
Recurrent neural networks work with sequences and memory. Using an LSTM model we explain how to process time series and sequential data, for example in spam detection.
We learn how to approach natural-language-processing tasks. We show examples such as a chatbot, automatic translation or sentiment analysis.
We learn how to prepare data properly before training a model. We go through handling missing values, categorical variables, scaling data and creating new features that can significantly affect the model’s results.
We get an overview of what deploying machine-learning models to real use involves. We also discuss monitoring model performance and checking that it works correctly.
The final block goes beyond classic machine learning – into the world of AI agents. We show how agents that plan their steps, use tools and solve practical tasks are created in LangChain.
We look at the principle of AI agents that can independently plan steps, use external tools and react to the results of their actions. Using the LangChain framework we show how to connect a language model to tools and create a simple agent for solving practical tasks.
After completing the course you will understand the basic principles of machine learning and artificial intelligence and be able to apply them in practice using Python.
You understand the basic principles of machine learning, classification, decision trees and unsupervised learning.
You learn to work with multilayer neural networks, convolutional networks and models for more complex AI tasks.
You try image recognition, natural-language processing, generative models and working with sequential data.
You learn to prepare data, evaluate model results, tune its behaviour and think about practical deployment.
In the course you work with tools from the Python ecosystem used for machine learning, neural networks, data processing, image work and experimenting with AI models.
The main language of the course and one of the most widely used tools for AI, automation and machine learning.
A practical environment for writing code, experimenting, visualising results and taking notes on models.
A library for numerical computation and working with arrays that forms the basis of many ML tools in Python.
A tool for loading, cleaning and preparing tabular data before training models.
A library for classification, decision trees, unsupervised learning and model evaluation.
An ecosystem for building and training neural networks, including models for images, text and deep learning.
It doesn’t end with the course. Even after completing it, you keep plenty of benefits you can rely on:
An official Turing Institut certificate documenting your newly acquired skills.
Recordings of all lessons and study materials remain yours forever – come back to them whenever you need.
A Discord full of people like you, a newsletter with IT news and exclusive members-only events.
There is currently no scheduled date for this course. Write to us or give us a call — we will let you know as soon as a new date is announced, and we are happy to arrange a date that suits you.
We have submitted this course to the Czech Labour Office programme. Once it is approved, the state can cover up to 76.73 % of the course price for you. Join the waitlist and you will be among the first to know.
How the funding worksNo. The course is designed to introduce you to AI concepts from the basics. Basic programming knowledge is enough – ideally in Python, but it is not a requirement.
Basic knowledge of programming in any language is enough. The practical part is in Python, the standard for working with AI. If you don't know Python yet, you can learn it alongside the course – the topics covered are not demanding in terms of programming.
No. Practical exercises take place in Jupyter Notebook, and for most tasks an ordinary laptop is enough. For more demanding models we use a cloud environment to which you get access.
It depends on the chosen format. In the weekday format classes take 2 × 2 hours a week; we recommend an additional 2–4 hours for assignments and self-study. The weekend format is 3 consecutive weekends (Saturday + Sunday, approx. 9:30–18:30), and between weekends we recommend 4–6 hours of home practice. You set your own pace within the course date – video recordings are also available.
The course is designed so that knowledge of advanced mathematics such as linear algebra or calculus is not necessary. Our lecturers are, however, well versed in these topics and are happy to answer questions.
These two courses sit side by side in the catalogue, but they teach different skills.
This course is about machine learning and neural networks. You build and train your own models — from classic algorithms (Bayes classifier, decision trees, unsupervised learning) through multilayer networks to convolutional networks for images, generative networks (GANs), autoencoders, time-series prediction (LSTM) and language processing. You learn to prepare data, evaluate how the model performs, tune it, and get an overview of deployment and monitoring. You work in Python with Jupyter Notebook, NumPy, Pandas, Scikit-learn and TensorFlow / Keras.
AI Agent Developer takes ready-made language models (LLMs) as the brain of an application and teaches them to act: plan steps, reach for tools, look up information in company data (RAG), remember context and cooperate with other agents. It covers LangChain, LangGraph, MCP, vector databases and then production operation — Kubernetes, the NVIDIA stack and preparation for the NCP-AAI certification.
The courses meet, but only at the edges. In the agents course you take an accessible journey from the neuron to the LLM and build a simple network so it is clear what language models are built on — but that does not replace this course. Conversely, here we end with one lesson on agents in LangChain. It is a taste of where you can go next, not the syllabus of the AI Agent Developer course.
A simple guide: if you want to understand how a model learns from data, train one yourself and be able to say whether it is good — stay here. If you want to build applications that already use LLMs, connect them to tools and data and run them — go for AI Agent Developer. They build on each other, but neither is a prerequisite for the other.
A more detailed comparison of all AI courses by goal can be found in the article Which AI course should I choose?.