Machine Learning Fundamentals in Python
At a glance
Certificate of attendance "Machine Learning Fundamentals in Python" (2 ECTS)
5 evenings, more details about the implementation
Language of instruction:
27.10.2022 17:00 - 20:00
03.11.2022 17:00 - 20:00
10.11.2022 17:00 - 20:00
17.11.2022 17:00 - 20:00
01.12.2022 17:00 - 20:00
Read the interesting interview with machine learning expert and course instructor Dr. Martin Rerabek here.
Objectives and content
Machine learning is a scientific discipline that allows a computational system to learn from data and act consequently without explicit programming. Nowadays, machine learning forms an inevitable part of life and many of us uses it many times every day without noticing it. In this course, we review basic principles of machine learning, create a fundamental intuition of presented algorithms, and apply acquired knowledge on real data.
Prerequisites for this course:
This course is aimed at students and/or professionals in all areas who want to start with and to develop themselves in machine learning using popular Python programming language. Participants should have some basic programming experience (not necessarily in Python). For very programming beginners, we offer a Python introductory course as a suitable prerequisite summarizing Python programming. This course is suitable for complete beginners without prior knowledge in machine learning. It will also suit well for those with intermediate level in relevant fields who want to deepen and solidify their knowledge.
The course focuses on the following technologies and techniques:
- Python programming language
- Data analysis, data visualization
- Machine learning algorithms
The course participants will:
- understand fundamental machine learning algorithms
- learn how to process, analyze, and visualize data in Python
- learn how to implement machine learning algorithms in Python
- Prior course materials - Introduction to Python: programming basics, scripting, writing functions in Python / data structures in Python / introduction to Python libraries scikit-learn, Pandas and Seaborn
- Data wrangling in Python
- data import/export from/to different sources
- data pre-processing
- data visualization
- Machine learning algorithms, supervised and unsupervised learning: regression, classification, clustering
- General machine learning good practices, model performance evaluation and validation
- Apply machine learning algorithms to real world data
CAS in Digital Life Sciences
This module is part of the CAS in Digital Life Sciences continuing education programme, but can also be attended independently of the CAS. Credit points earned for this module can be credited to the CAS course at a later date, provided the relevant general conditions are fulfilled.
More information here: CAS in Digital Life Sciences
The course is strongly based on applied exercises. In addition to traditional lectures, the students will experience:
- parctical programming exercises
- group work
- self-study (preparation and follow-up)
- final project
More details about the implementation
There are 5 lessons organized once a week on Thursday from 5:00pm to 8:00pm. Prior to the course start, participants will receive materials to refresh their Python skills.
After 4 lessons the students work on their final project for about two weeks. The final project presentation will be held during the last lesson.
Enquiries and contact
Martin Rerabek is an experienced researcher with more than 12 years of practical and academic experience in the field of machine learning using python.
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