Machine Learning focuses on constructing algorithms for making predictions from data. These algorithms usually require huge amount of data to analyse, thus demanding for high computational costs and requiring easily scalable solutions to be effectively applied. These factors favoured a more and more increasing interest in scaling up machine learning applications.
Scikit-learn is one of the most popular machine learning library in Python, providing implementations for several machine learning methods, along with datasets and (performance) evaluation algorithms.
In this talk some recipes to scale up machine learning algorithms with scikit-learn will be presented. The talk will go over several examples and case studies that will be presented in a problem-to-solution way in order to likely engage discussions during and after the talk.
The talk is intended for an intermediate level of audience. It requires (very) basic math skills and a good knowledge of the Python language. Good knowledge of the numpy and scipy packages is also a plus.