Guide to choosing an AI course

Which AI course should you choose?

Not all artificial intelligence courses lead to the same goal. In some you mainly work with data, in others you train models, build AI agents or bring AI into software development. Here you will quickly find out which path matches what you want to be able to do.

Choose by goal Compare all 5 courses
  • 5 clearly distinct AI paths
  • Recommendations by goal and experience
  • Up-to-date course content and scope
Start with the goal, not the name

What do you actually want to be able to do after the course?

Pick the outcome you want to achieve. Each option takes you straight to the details of the most suitable course.

Mine and analyse data

I want to retrain for data analysis, work with data in Python and SQL, create visualisations, run A/B tests and get an introduction to predictive models.

Recommended courseData Analysis and Artificial Intelligence

Build and train my own AI models

I want to understand how a model learns from data and work with machine learning, deep learning, images and text.

Recommended courseMachine Learning and Neural Networks

An overview from machine learning to agent systems

I want to extend my qualifications with AI and apply it across a range of fields – connecting model principles, LLMs, prompt engineering, APIs, automation and my own AI agents.

Recommended courseAI for Developers, Science and Data Analytics

Build AI agents and agent systems

I want to retrain for AI agent development and learn to create agents with memory and tools, RAG systems and multi-agent workflows, and run them safely in production.

Recommended courseAI Agent Developer

Make full use of AI in software development

I already develop software and want to master Claude, Codex, Cursor, subagents, context management and an AI-first development cycle.

Recommended courseVibe Coding and AI in Software Development
Skills map

Data, models, agents and AI development are not the same thing

The individual areas complement each other, but each solves a different part of the problem. The best choice depends on where you want your core competence to be.

1. DataFind, clean, join, analyse and clearly explain information.
2. ModelsTeach an algorithm to find patterns, predict and work with images, text or time series.
3. AI agentsGive an LLM tools, memory and data so that it plans steps and acts on its own.
4. AI-first developmentDirect coding agents and integrate them into the design, implementation, testing and operation of software.

The broad course "AI for Developers, Science and Data Analytics" connects models and agents above all. The other courses go deeper within their main area.

Visual map

How do the AI courses overlap?

The three circles represent the main areas of AI education. The numbers show where each course really has its centre of gravity; the full name and explanation are in the legend.

Overlap of the five Turing Institut AI courses Three circles represent data and analytics, models and machine learning, and AI agents and applications. The set names and short descriptions are placed outside the circles. Point one lies mostly in the data and analytics area and slightly overlaps models and machine learning, point two lies in the standalone models area, point three in the clear intersection of models and machine learning with AI agents and applications, point four in the AI agents and applications area right on the edge with models and machine learning, and point five inside the AI agents and applications area. Data & analytics SQL · statistics · visualisation Models & ML training · deep learning 1 2 3 4 5 AI agents & applications LLMs · tools · RAG · workflows
1Data Analysis and Artificial IntelligenceData & analytics · slight overlap into Models & ML 2Machine Learning and Neural NetworksModels & ML · training and evaluating models 3AI for Developers, Science and Data AnalyticsIntersection of Models & ML and AI agents & applications 4AI Agent DeveloperAI agents & applications · on the edge with Models & ML 5Vibe Coding and AI in Software DevelopmentAI agents & applications · development workflow

How to read the diagram: the position shows the main focus, not the percentage of the syllabus. Course no. 1 is built mainly on data and analytics but overlaps slightly into Models & ML thanks to the basics of predictive modelling. Course no. 3 sits in the clear intersection of Models & ML and AI agents & applications – it connects the principles of models and LLMs with building agents, automation and APIs. Course no. 4 clearly belongs to AI agents and applications, but lies right on the edge with Models & ML – building production agents also requires understanding LLMs and how they are run. Course no. 5 stays inside the agents area and focuses on using them in software development.

Possible study paths

The courses build on each other naturally

Three practical paths depending on where you are heading. This is not a mandatory order or a formal prerequisite.

01Career path

I want a career in data and data science

Data AnalysisMachine Learning

First you master working with real data, SQL and statistics, then you go deeper into models and neural networks.

02Career path

I want to be an AI / agent engineer

Machine LearningAI Agent Developer

You gain a deeper understanding of models and then use them as the foundation of robust agent systems.

03Career path

I am a developer and want AI in practice

Vibe Coding and AI in DevelopmentAI Agent Developer

First you transform your own development workflow, then you can specialise in products built on agents.

Still torn between two courses?

Tell us about your experience and what you want to achieve. We will recommend a suitable path without unnecessary jargon.

info@beeit.cz · +420 732 813 373
Contact usAll courses