Beschreibung
Learn statistical analysis, data visualization, and how to train machine learning algorithms in Python
Learning Goals:
- How to differentiate between data science, machine learning, and artificial intelligence
- How to calculate descriptive statistics and confidence intervals and prepare them graphically
- How to work in VSCode and Python with version control in a team on an analysis project
- How to perform linear regressions and use imputation methods for missing values
- How to train simple neural networks with Python and TensorFlow and use them for predictions
What You Get
You'll receive a practical introduction to data science and machine learning using VSCode and Python. You will work in a team on an analysis project and practically implement the various steps relevant to data science or machine learning projects using current tools and libraries.
All required software and online course content is free. The use of free access to Claude.ai, GitHub, and possibly other free platforms is assumed.
This course is part of the opencampus.sh Machine Learning Degree program. Participants in the Machine Learning Degree program receive preferential access to this course. More information about the degree program can be found here.
What You Should Bring
You should complete the first chapter of the course Introduction to Python BEFORE the course begins.
Prior programming knowledge is useful to keep up with the course progress. If you have no programming experience, the pace will be very fast for you. You should therefore plan for additional time to deepen your understanding through the online resources provided (typically more than 10 hours of effort per week!). Once teams are formed and analysis projects begin (from the 3rd or 4th session), additional time should be planned to work on the project.
To receive a Certificate of Achievement (or ECTS) for this course, active participation is expected, you may not miss more than two course sessions, and you must complete a practical project in a team of two to four people. At the end of the course, the project must be presented and well-documented source code for the project must be submitted. (Certificates of attendance are not issued for this course.)