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

New to Scientific Python?

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.

Quick start

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

• 2.1. An introduction to machine learning with scikit-learn
  • 2.1.1. Machine learning: the problem setting
  • 2.1.2. Loading an example dataset
  • 2.1.3. Learning and predicting
  • 2.1.4. 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

Machine Learning Cheat Sheet (for scikit-learn)

This flowchart is useful for newcomers regarding how to go about solving problems using scikit-learn. It provides a rough guide on how to approach problems and which estimators to try out on your data. Click the image below to begin..

• 2.3. Choosing the right estimator

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.