Machine Learning with Scikit-Learn (material for pydata Amsterdam 2016)
Jupyter Notebook Python
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solutions
.gitignore
Advanced Scoring.ipynb
Combining Pipelines and GridSearchCV.ipynb
Cross-validation.ipynb
Custom Estimators.ipynb
Grid Searches for Hyper Parameters.ipynb
LICENSE
Out Of Core Learning for Text.ipynb
Out Of Core Learning.ipynb
Preprocessing and Pipelines.ipynb
README.md
Working With Text Data.ipynb
environment.yml
pydata-amsterdam-advanced-sklearn.odp
pydata-amsterdam-advanced-sklearn.pdf

README.md

Machine Learning in Python with scikit-learn

This repository contains material for the PyData Amsterdam 2016 tutorial

Machine learning in Python with scikit-learn

by Andreas Mueller.

13:30-14:15 Saturday, 12/03/2016

Location: Room2

It is recommended that you update the materials before the course, as they might change in the days leading up to the conference.

Please bring a laptop with a working installation of Python (2.7, 3.4 or 3.5). The following packages are required:

  • scikit-learn >= 0.16
  • matplotlib >= 1.3
  • numpy >= 1.5
  • IPython >= 4.0
  • Jupyter Notebook >= 4.0

The easiest way to install all requirements is to install the free Anaconda Python distribution: https://www.continuum.io/downloads (OS X, Windows, Linux)

Please download the material prior to arriving to the tutorial, and make sure you can run the notebooks. To run a notebook, start Jupyter Notebook and browse to the folder to which you downloaded it.