This course serves as a general introduction to machine learning in Python and requires basic programming experience, preferably in Python. The materials below have been used for several machine learning courses such as the machine learning training course at Mbarara University of Science and Technology and the UGAIN machine learning course and deep learning course at Ghent University. Most course materials have been re-used and adapted from the course Machine Learning for the Life Sciences, given at the Faculty of Bioengineering at Ghent University.
The course materials require Python >3 and several data analysis packages such as Pandas, NumPy, Matplotlib, Scikit-learn and PyTorch.
To get started, simply click on the course material links below in order to initiate your session in Google Colab. Google Colab is a hosted Jupyter Notebook service that requires no setup to use and provides free access to computing resources. To get started with Google Colab, we recommend the following tutorial.
The course materials can also be downloaded and run locally - for more information see the addendum in the practical Introduction to Python.
- Introduction to Python
- Data Preprocessing and Nearest Neighbors
- Linear Regression
- Linear Classification
- Resampling Methods for Model Evaluation
- Regularization Methods
- Tree-based Methods
- Unsupervised Learning
- Introduction to Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Generative Models