More talks in the program:
10:00 - 11:00
This presentation shows how to quickly build Machine Learning applications with Python and how we can understand what is happening ‘under the hood’ using Python modules as well. Two examples will be presented: unsupervised and supervised learning for text classification.
It is fascinating how fast you can build a text analyzer with Python and Scikit to then apply unsupervised learning. A common approach is to first build numerical representations of the text, and then to apply standard statistical (or machine learning) techniques. I also wanted to know how the intermediate data looks like. Therefore, I built a little example that writes the internal data into an Excel file to better visualize and understand (down to the numerical values) how the feature extraction and the cluster building work together.
Another big benefit offered by Python are Deep Learning packages like Keras, which we use for a
supervised learning examples. You can quickly set up a complex neural network and have its construction, training and testing in less than 20 lines of code.