Machine Learning in Python
Learn to program in Python and dive into the basics of machine learning. Gain practical skills and knowledge of algorithms that you can apply in IT, data analytics, and science.
9 093 CZK
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October 2026
Part-TimeForm of study9 093 CZK incl. VATPriceKolbenova 942/38a, Praha 9Adress
About the course
The Machine Learning in Python Micro-Credential offers a comprehensive introduction to the principles and methods of machine learning with a strong focus on practical application. Participants will first learn the basics of Python 3, the standard language for data analysis and artificial intelligence. They will then move on to key machine learning concepts – from data preparation and algorithm selection to model evaluation. Emphasis is placed on understanding each step of the process and applying it to real-world tasks, enabling participants to gain practical skills applicable in IT, analytics, and research.
15 ECTSTotal ECTS creditsBeginnerLevelCZLanguage
Course outline
1. Module: Basics of programming in Python
Introduction to PythonIntroduction to the language and insight to the context of other programming languages, differences across operating systems, modules
Natural data types, python data model, fixed and variable typesSeminar work assignment
The remaining data types (List, tupple, dict…) and the construction of matrix considerations and data storageData types from basic modules (matrix, dataframe)
Logical operations, conditions, cycles
Cycles, exceptions, mathematical operations, basic modules and numpy
User-defined functionsFunction parameters, function body, global and local function environment, lambda functions, recursive functions
Import and export of data, csv, json and other data formats
Data visualization and matplotlib, API and other graphics libraries
Creating your own module, analysis of default modules
Introduction to Object Oriented Programming (OOP)
Properties of OOP, examples of inheritanceCreating your own classes, creating your own exceptions in the context of OOP
Modules for linear and nonlinear optimizationPython as a tool for machine learning and an example of a neural network
2. Module: Introduction to machine learning
Definition of machine learning, its aspects and phases
Machine learning approaches and selection of suitable algorithms
Interpretation and verification of training results
Decision trees
Linear regression
Neural networks
Clustering
Dimensionality reduction
Applications of machine learning in various industries
Technical Requirements
No prior knowledge or tools are required to complete the course – just an open mind and a willingness to think.
Your own laptop or tablet
To comfortably work on your case study and access course materials.
Stable internet connection
The course is partially online, so a reliable connection – and ideally a quiet space for consultations – is recommended.
That’s all. We’ll provide the rest.
Who is this course for?
Students and beginner developers who have basic Python skills and want a confident first step into the world of machine learning. They will learn to work with data, experiment with basic algorithms, and understand when and why to use them – all through practical, real-world examples.
Data analystsAnalysts experienced with Excel, SQL, or Power BI who want to move into predictive analytics. The course provides a Python workflow, basics of modeling, result validation, and tips on how to integrate machine learning into existing reports and operational processes.
Software developers and engineersDevelopers who need to add “intelligent” features to applications – such as recommendations, classification, or anomaly detection. They will learn how to choose approaches, prepare data, and deploy simple models in a way that is maintainable and performance-efficient.
Product managers and consultantsProfessionals seeking to understand the possibilities and limits of ML. They will gain the terminology, be able to better define requirements for their team, assess model quality and risks, and decide when ML is worthwhile and when a simpler solution is more appropriate.
Teaching methods
The course combines self-study with guided practice, allowing participants to master key concepts and apply them through hands-on experience.
- Self-study with clearly structured materials
- Tutorials and individual consultations with the instructor
- Assessed assignment – own project proposal
- Final online test and in-person workshop
