scikit-learn Tutorials

• An introduction to machine learning with scikit-learn
  • Machine learning: the problem setting
  • Loading an example dataset
  • Learning and predicting
  • Model persistence

• A tutorial on statistical-learning for scientific data processing
  • Statistical learning: the setting and the estimator object in the scikit-learn
  • Supervised learning: predicting an output variable from high-dimensional observations
  • Model selection: choosing estimators and their parameters
  • Unsupervised learning: seeking representations of the data
  • Putting it all together
  • Finding help

• Working With Text Data
  • Tutorial setup
  • Loading the 20 newgroups dataset
  • Extracting features from text files
  • Training a classifier
  • Building a pipeline
  • Evaluation of the performance on the test set
  • Parameter tuning using grid search
  • Exercise 1: Language identification
  • Exercise 2: Sentiment Analysis on movie reviews
  • Exercise 3: CLI text classification utility
  • Where to From Here

Note

Doctest Mode

The code-examples in the above tutorials are written in a python-console format. If you wish to easily execute these examples in IPython, use:

%doctest_mode

in the IPython-console. You can then simply copy and paste the examples directly into IPython without having to worry about removing the >>> manually.