# A tutorial on statistical-learning for scientific data processingΒΆ

Statistical learning

Machine learning is a technique with a growing importance, as the size of the datasets experimental sciences are facing is rapidly growing. Problems it tackles range from building a prediction function linking different observations, to classifying observations, or learning the structure in an unlabeled dataset.

This tutorial will explore statistical learning, that is the use of machine learning techniques with the goal of statistical inference: drawing conclusions on the data at hand.

`sklearn` is a Python module integrating classic machine
learning algorithms in the tightly-knit world of scientific Python
packages (numpy, scipy, matplotlib).

Warning

In scikit-learn release 0.9, the import path has changed from scikits.learn to sklearn. To import with cross-version compatibility, use:

```
try:
from sklearn import something
except ImportError:
from scikits.learn import something
```

- Statistical learning: the setting and the estimator object in the 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