• Interpretable Machine Learning with Python

    Online event

    Full Title: Interpretable Machine Learning with Python — What qualities make for a highly rated chocolate bar? Abstract: In the first part of this workshop, we will provide some context to what value interpretation can provide to machine learning practitioners. In a nutshell: models learn from our data, and we can learn a lot from our models… but only if we interpret them! In part two, we kick off the more hands-on portion of the workshop! We will train classification models with chocolate bar ratings to identify which ones are highly recommended by chocolate experts: one SVM model on tabular data and a LightGBM NLP model. We will then employ popular model-agnostic interpretation methods to interpret these "black-box" models' decisions such as SHAP and Local Interpretable Model-Agnostic Explanations (LIME). That way, chocolatiers can understand what features correlate the most with these high ratings. Short Bio: Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, incubated by Harvard Innovation Labs. Serg is passionate about providing the often-missing link between data and decision-making. His book titled "Interpretable Machine Learning with Python" is scheduled to be released in early 2021 by UK-based publisher Packt. Join from a PC, Mac, iPad, iPhone or Android device: Please click this URL to join. https://zoom.us/j/92062123325?pwd=Q2RINXVlTlp3NkFQRllOUFRqZHBSQT09 Passcode:[masked] Or join by phone: Dial(for higher quality, dial a number based on your current location): US: [masked] or [masked] or [masked] or [masked] or [masked] or [masked] Webinar ID:[masked] International numbers available: https://zoom.us/u/abs6ysH89i Or an H.323/SIP room system: H.323:[masked] (US West)[masked] (US East)[masked] (India Mumbai)[masked] (India Hyderabad)[masked] (Amsterdam Netherlands)[masked] (Germany)[masked] (Australia)[masked] (Singapore)[masked] (Brazil)[masked] (Canada)[masked] (Japan) Webinar ID:[masked] Passcode:[masked] SIP:[masked]@zoomcrc.com Passcode:[masked]