Although machine learning is a broad subject with many different applications, there are key aspects that should be considered for any ML-algorithm.
For example, before applying ML the domain and data must be analyzed, and additional processing may be required.
In this talk, I'll present some common machine learning paradigms using scikit-learn. Data preparation, decision trees, random forests, neural networks, training and evaluation of supervised learning will all be covered.
This presentation will primarily serve as an introduction to machine learning in general and to the respective Python frameworks.
