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

Each of the following tutorials is meant to be autonomous and the target audiences are not expected to have same level of mathematical proficiency.

Beginner tutorial

Ten minutes tutorial to quickly get started with scikit-learn.

• 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 Inference tutorial

One day long tutorial on Machine Learning for people with a scientific background (graduate students and researchers).

• 2.2. Scikit-learn tutorial: statistical-learning for sientific data processing
  • 1. Statistical learning: the setting and the estimator object in the scikit-learn
  • 2. Supervised learning: predicting an output variable from high-dimensional observations
  • 3. Model selection: choosing estimators and their parameters
  • 4. Unsupervised learning: seeking representations of the data
  • 5. Putting it all together
  • 6. Finding help

scikit-learn for Astronomical Data Analysis

One day long tutorial on Machine Learning with scikit-learn with applications to astronomy.

• 2.3. Using scikit-learn with Astronomical Data
  • 2.3.1. Astronomy Tutorial setup
  • 2.3.2. Machine Learning 101: General Concepts
  • 2.3.3. Machine Learning 102: Practical Advice
  • 2.3.4. Classification: Learning Labels of Astronomical Sources
  • 2.3.5. Regression: Photometric Redshifts of Galaxies
  • 2.3.6. Dimensionality Reduction of Astronomical Spectra
  • 2.3.7. Exercises: Taking it a step further

Machine Learning for Text Analytics with scikit-learn.

One day long tutorial on Machine Learning with scikit-learn with applications to text analytics (classification, clustering, topic modeling).

• 2.4. Machine Learning for Text Analytics with scikit-learn
  • 2.4.1. Tutorial setup
  • 2.4.2. Machine Learning 101: General Concepts
  • 2.4.3. Working with text data
  • 2.4.4. Exercises
  • 2.4.5. How to select the right algorithm for the task
  • 2.4.6. Finding help