Machine Learning with Scikit-Learn (material for pydata Amsterdam 2016)
Jupyter Notebook Python
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Advanced Scoring.ipynb
Combining Pipelines and GridSearchCV.ipynb
Custom Estimators.ipynb
Grid Searches for Hyper Parameters.ipynb
Out Of Core Learning for Text.ipynb
Out Of Core Learning.ipynb
Preprocessing and Pipelines.ipynb
Working With Text Data.ipynb

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: (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.