Introduction to Machine Learning and Data Visualization with Python

Martina G. Vilas
Code

Abstract

This short interactive tutorial will show you how to use the scikit-learn Python package to perform basic machine learning analysis. It will also cover how to visualize your results with the matplotlib and seaborn Python packages.

Date
Jun 16, 2021
Event
OHBM BrainHack 2021
Location
OHBM BrainHack 2021

You can access the code for this tutorial here.

Structure

Notebook Content
1. Preliminaries Introduction to the tutorial and dataset
2. Core concepts Estimators, regression, classification and clustering in scikit-learn
3. Pipelines Transformers, preprocessing, feature selection, feature engineering, dimensionality reduction and pipelines in scikit-learn
4. Overfitting The problem of overfitting, cross-validation, regularization and hyper-parameter tuning in scikit-learn
5. Visualization Basic components of a matplotlib plot and basic plots with seaborn

Learning goals:

  • Learn how to load and prepare your data for machine learning analysis with scikit-learn.
  • Learn how to perform regression, classification and clustering analysis with scikit-learn.
  • Learn the concepts of regularization, cross-validation, hyper-parameter tuning, and how to implement them using scikit-learn.
  • Learn how to inspect and evaluate a machine learning model.
  • Learn how to plot your results with matplotlib and seaborn.

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