This is a tutorial for scikit learn in Python. It covers basic concepts as well as use cases.

Agenda:

• Loading datasets
• Splitting dataset
• Preprocessing
  • Encoding variables into
  • Intro to Estimators interface
  • Feature scalling
• Support Vector Machines (SVMs)
  • Intro & Application to SVMs
  • Plotting
• Model Evaluation
  • Evaluation metrics
  • Naive evaluation
  • K-fold evaluation with hyperparameter grid search

A few words before starting:

• I am not an expert in the topic (disclaimer).
• Thanks to Dimis Koimtzoglou for advices during the preparation.

Author: Adam Zika, JADS

** To follow the tutorial, follow the file of: Python&Scikit - complete use case with SVM.ipynb **