A highly practical course introducing the key areas to be aware of in Python machine learning. The focus is on solving real-life examples using the scikit-learn library.
Content
- An introduction to machine learning,
- prior to data processing,
- Machine learning: basic regression models,
- Machine learning: basic classification models,
- decision trees and random forests,
- unsupervised learning,
- model evaluation,
- Model refinement (Grid search, cross-validation…),
- processes and pipelines,
- an introduction to deep learning.
Recommended prior knowledge:
knowledge of Python, knowledge of tools for data analysis (pandas, numpy) or participation in the Data Analysis in Python course.
Learning objectives
- use the Python programming language and the scikit-learn library in hardware,
- understand the basic principles and methods of machine learning.
Target group
- anyone interested in machine learning,
- Anyone who wants to develop a career in data science.