Tutorial: scikit-learn Tutorials
scikit-learn Tutorials
- An introduction to machine learning with scikit-learn
-
A tutorial on statistical-learning for scientific data processing
- Statistical learning: the setting and the estimator object in 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 newsgroups 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
- Choosing the right estimator
- External Resources, Videos and Talks
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.
© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/tutorial/index.html
