Ausbildung - Data Science - Foundations & Machine Learning

Erreechten Niveau

Mëttelstuf

Dauer

96,00 Stonn(en)

Sprooch(e) vun der Déngschtleeschtung

Nächst Sessioun

Wien organiséiert dës Formatioun?

Au CNFPC, nous construisons les compétences de demain. Un lieu unique où salariés, chefs d’entreprise, demandeurs d’emploi, jeunes adultes et grand public se forment, innovent et évoluent ensemble. Grâce à notre collaboration avec l’ADEM, nos entreprises partenaires et nos experts de terrain, nos formations répondent aux besoins réels du marché du travail, aujourd’hui et demain. Des programmes concrets, innovants et tournés vers l’action, dispensés par des professionnels qui partagent leur expérience du terrain.

Méi gesinn

Den Inhalt vun dëser Säit ass net op Lëtzebuergesch verfügbar

U wie riicht sech d'Formatioun?

Employees, adults in career transition, or professionals aiming to move into roles such as Data Scientist, Advanced Data Analyst, or Machine Learning Engineer; profiles from IT, science, mathematics, engineering, or business analytics backgrounds.

Virkenntnisser

Basic computer skills, knowledge of logic, mathematics, and statistics. Ideally, participants should have completed a data analyst module or equivalent

Ziler

At the end of the training, participants will be able to:
- Install and use a Python environment for data science
- Manipulate data with fundamental tools (Anaconda, Jupyter, Pandas)
- Master the mathematical foundations necessary for machine learning
- Implement and evaluate different supervised learning models
- Work on artificial neural networks
- Apply unsupervised learning
- Visualise multidimensional data
- Complete a comprehensive data science project and defend it

Inhalt

Software basic programming / Mathematics / Machine Learning / Final Project

Behandelt Punkten

Module 1 | Software basic programming |16h
- Anaconda
- Jupyter
- Python

Module 2 | Mathematics | 16h

Module 3 | Machine Learning | 48h
- Introduction Basics concepts and notations
- Basics models for supervised learning
- Multilayer Artificial Neural Networks
- Unsupervised Learning
- Visualising High-dimensional Data

Module 4 | Final Project | 24 h

Pedagogesch Methoden

The approach combines theory, hands-on exercises, and an individual project, with interactive discussions to encourage practical application of the skills learned.

Certificat, Diplom

Training Certificate

Organisatiounsmodus

Cancellation:
In case of cancellation or absence from the course, the registration fees are fully due if the cancellation is not made at least 72 hours before the start of the course. Absences properly justified with a medical certificate entitle the participant to a full refund of the registration fees.