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Introduction to Machine Learning and Data Visualization with Python

Binder

👋 Welcome! 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.

This tutorial was developed to be presented at the 2021 edition of the OHBM BrainHack 🧠

Structure of this tutorial

  • The content of this tutorial is divided into the following notebooks:
Notebook Content
01. Preliminaries Introduction to the tutorial and dataset
02. Core concepts Estimators, regression, classification and clustering in scikit-learn
03. Pipelines Transformers, preprocessing, feature selection, feature engineering, dimensionality reduction and pipelines in scikit-learn
04. Overfitting The problem of overfitting, cross-validation, regularization and hyper-parameter tuning in scikit-learn
05. Visualization Basic components of a matplotlib plot and basic plots with seaborn
  • Each notebook provides small comprehension exercises, and a final integration exercise.

Executing the notebooks

  • To run these notebooks in Binder head here:

    Binder

  • To run these notebooks locally, follow these steps (you need to have installed git and python in your computer):

$ git clone https://github.com/martinagvilas/ML-Vis-BrainHack-2021.git
$ cd {path_to_cloned_folder}
$ pip install -r requirements.txt

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.

Pre-requisites

  • For this tutorial, we assume you have:
    • Basic knowledge of machine learning concepts. For example, you know the difference between supervised/unsupervised learning, or the difference between classification, regression and clustering models.
    • Basic experience with Python.
  • We also assume that you have seen the video "A tutorial on machine learning" by Laura Suarez.

Code of conduct

This event follows the OHBM BrainHack code of conduct. Please make sure to read it before participating in our session.

Get in touch

If you run into any problems while executing this tutorial, please open an issue describing the problem.

We also welcome any enhancements, ideas or discussions on the structure or contents covered in the notebooks.