A guided tour through of some of the features in scikits.learn and how they are being used to solve real-world problems: from handwritten digits classification to stock market prediction.
Target audience : Engineers and scientists using Python for scientific and numerical computing. No knowledge needed in statistical learning.
Prerequisites: Being able to code scripts and function in Python. Basic knowledge of numpy and matplotlib.
Software requirements: IPython, scikits.learn, matplotlib.
- Datasets in scikit-learn.
- Classification: Support Vector Machines and its variants. Example: recognizing handwritten digits.
- Clustering: KMeans, Affinity Propagation. Example: finding structure in the stock market.