For written tutorials, see the Tutorial section of the documentation.
Scientific Python 的新手？
For those that are still new to the scientific Python ecosystem, we highly recommend the Python Scientific Lecture Notes. This will help you find your footing a bit and will definitely improve your scikit-learn experience. A basic understanding of NumPy arrays is recommended to make the most of scikit-learn.
There are several online tutorials available which are geared toward specific subject areas:
> A three minute video from a very early stage of the scikit, explaining the basic idea and approach we are following.
Introduction to statistical learning with scikit-learn by Gael Varoquaux at SciPy 2011
> An extensive tutorial, consisting of four sessions of one hour. The tutorial covers the basics of machine learning, many algorithms and how to apply them using scikit-learn. The material corresponding is now in the scikit-learn documentation section 关于科学数据处理的统计学习教程.
Statistical Learning for Text Classification with scikit-learn and NLTK (and slides) by Olivier Grisel at PyCon 2011
> Thirty minute introduction to text classification. Explains how to use NLTK and scikit-learn to solve real-world text classification tasks and compares against cloud-based solutions.
> 3-hours long introduction to prediction tasks using scikit-learn.
> Interactive demonstration of some scikit-learn features. 75 minutes.
> Presentation using the online tutorial, 45 minutes.