PyData Berlin 2023 March Edition

Welcome to the March edition of Pydata Berlin meetup !!

For everybody to feel safer, we recommend you test yourself against COVID-19 before coming to the event. A self-test or a rapid antigen test would suffice. And please refrain from coming to the event if you feel unwell.

Doors open at 18:00 and the pizzas will be served at 18:30.

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Talks :
Soledad Galli: Feature engineering for machine learning with open-source
Feature engineering is the process of transforming variables and extracting and creating new features from data to train machine learning models. Raw data is almost never suitable to train machine learning models. Instead, data scientists devote a huge part of their time to data pre-processing and feature creation.
Imputing missing data, encoding categorical data, and transforming or discretizing numerical data are all examples of feature engineering. Feature engineering also includes extracting features from texts, time series, and relational databases.
In this talk, I will introduce the main feature engineering and feature creation methods and then highlight the main open-source Python libraries for feature engineering, stressing their advantages and suitability for different types of data.

Soledad Galli is the developer of the open-source Python library Feature-engine. Feature-engine is a feature engineering and selection library that is currently downloaded 130k+ times a week, has 1.2k stars, and 40+ contributors.
Sole is also the lead instructor at Train in Data, where she teaches online courses that have enrolled 46k+ students worldwide and consistently receive good reviews. She is also the author of Packt’s Python Feature Engineering Cookbook and Leanpub’s Feature Selection for Machine Learning book.
As a data scientist, Sole has developed and put into production machine learning models to assess insurance claims, credit risk, and prevent fraud. Soledad received a Data Science Leaders' award in 2018 and was recognized as one of LinkedIn's voices in data science and analytics in
2019. She is passionate about sharing her machine learning knowledge.

Gianluca Detommaso: Fortuna, a Library for Uncertainty Quantification
At AWS we released Fortuna, an open-source library for uncertainty quantification. Fortuna provides calibration methods, e.g. conformal prediction and temperature scaling, as well as several scalable Bayesian inference solutions. In this talk we will give a general overview of the field, and show how you can use Fortuna to assess the reliability of your model predictions.

Gianluca Detommaso has been working on Bayesian inference since the beginning of his PhD in Mathematics at the University of Bath. After his PhD, he worked for two years at Amazon on probabilistic modelling for pricing applications, and over one year at AWS on uncertainty quantification (UQ) in deep learning. In his spare time, Gianluca likes to practice sports, eat great food and learn new skills.

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