2. Tutorials: From the bottom up with scikit-learn

Quick start

In this section, we introduce the machine learning vocabulary that we use through-out scikit-learn and give a simple learning example.

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

Statistical-learning Tutorial

This tutorial covers some of the models and tools available to do data-processing with Scikit Learn and how to learn from your data.

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

External Tutorials

There are several online tutorials available which are geared toward specific subject areas:

• Machine Learning for NeuroImaging in Python
• Machine Learning for Astronomical Data Analysis

Videos

Videos with tutorials can also be found in the Videos section.

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.