Machine Learning Fundamentals in Python
Machine learning is a scientific discipline that allows a 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. This course introduces basic principles of machine learning and creates a fundamental intuition of its algorithms. Provided topics include:
- supervised learning: parametric and non-parametric algorithms, regression, classification, support vector machines
- unsupervised learning: dimensionality reduction, clustering
The course will also focus on general machine learning good practices, model performance evaluation and validation. To introduce machine learning algorithms, a Python programming language is used. Basics of Python programming including data processing and analysis and data visualization is also part of this course.
At a glance
Qualification : Certificate of attendance "Machine Learning fundamentals in Python" (2 ECTS)
Start : on request
Duration : 6 evenings
Costs : CHF 1'150.00
Language of instruction : English
Objectives and content
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. It is suitable for complete beginners without prior knowledge in Python and/or 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
- 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
- Introduction to Python: programming basics, scripting, writing functions in Python / data structures in Python / introduction to Python libraries scikit-learn, Pandas and Seaborn
- Data manipulation in Python – data import/export from/to different sources
- Data pre-processing for analysis and data visualization in Python
- Machine learning algorithms, supervised and unsupervised learning: regression, classification, clustering
- Model regularization and evaluation: cross-validation, bias/variance, over-fitting
- 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
There are 6 lessons organized once a week on Thursday from 5:30pm to 8pm. After 5 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
Dr. Martin Rerabek, firstname.lastname@example.org
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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