skill track 05
Machine Learning
Introduction to Machine Learning.
Description
A series of notebooks designed to build your machine learning knowledge, focusing on fundamental algorithms and techniques using Python with scikit-learn, NumPy, and Matplotlib.
By the end of these notebooks, you should feel more comfortable with:
▸Key machine learning concepts: linear models, decision trees, SVMs, ensemble methods, and unsupervised learning
▸Implementing and optimizing ML models using scikit-learn, including preprocessing, training, hyperparameter tuning, and evaluation
▸Applying machine learning to medical problems such as disease prediction, patient segmentation, and anomaly detection in health data
Announcements
In this skill track, the notebooks build on each other. Therefore, complete them in the order given!
Requirements
- ▸You should have completed all previous skill tracks
- ▸Data from open-source databases is loaded automatically at the beginning of each notebook in Google Colab
- ▸Have a look at the theoretical basics before you start with the notebooks
Syllabus
References
MedMNIST v2: Jiancheng Yang et al. (2023), Scientific Data · Jiancheng Yang et al. (2021), IEEE ISBI