33:28 Kirstie Whitaker: The Turing Way: A how to guide for reproducible research | PyData London 2019 PyData
38:54 Elina Naydenova: Bridging health inequalities through machine learning | PyData London 2019 PyData
1:24:37 Michal Mucha: Build and Deploy an End-to-end Streaming NLP Insight System | PyData London 2019 PyData
1:28:40 Raoul-Gabriel Urma, Kevin Lemagnen: Adv. Software Testing for Data Scientists | PyData London 2019 PyData
1:28:17 Anna Veronika Dorogush: Mastering gradient boosting with CatBoost | PyData London 2019 PyData
43:50 Dr. Egor Kraev: Choose the right neural generative model for your problem | PyData London 2019 PyData
1:30:10 Chris Fonnesbeck: An introduction to Markov Chain Monte Carlo using PyMC3 | PyData London 2019 PyData
1:24:08 Ian Ozsvald: A gentle introduction to Pandas timeseries and Seaborn | PyData London 2019 PyData
38:47 Alejandro Saucedo: Guide towards algorithm explainability in machine learning | PyData London 2019 PyData
34:49 Irene Iriarte Carretero: Data Science for Dinner: Recommending Recipes | PyData London 2019 PyData
38:29 Cheuk Ting Ho- 3 things about Chatbot (you don't have to learn it the hard way) - PyData London 2019 PyData
38:38 Jan Freyberg: Active learning in the interactive python environment | PyData London 2019 PyData
38:16 Maciej Arciuch, Karol Grzegorczyk: Embeddings! Embeddings everywhere! | PyData London 2019 PyData
34:56 Jake Coltman- What Failure Taught Me About Building High-Stakes Models - PyData London 2019 PyData
40:42 Jonny Brooks-Bartlett- How Deliveroo improved the ranking of restaurants - PyData London 2019 PyData
41:47 Maarten Breddels & Jovan Veljanoski- A new approach to DataFrames and pipelines - PyData London 2019 PyData
38:38 Mahan Hosseinzadeh- Prophet at scale to tune & forecast time series at Spotify - PyData London 2019 PyData
33:58 Ajay Thampi: Interpretable AI or How I Learned to Stop Worrying and Trust AI | PyData London 2019 PyData
37:41 James Powell: Because You Can Run, You Can't Hide: Some Musings on API Design | PyData London 2019 PyData
40:28 Matthew Hertz, Alla Maher: Kafka in Finance: Over 1 Billion messages a day | PyData London 2019 PyData
39:11 Trevor Sidery, Guillermo Barquero: Productionising Data Science at Scale | PyData London 2019 PyData
32:23 Ruben van de Geer: A Primer (or Refresher) On Linear Algebra for Data Science | PyData London 2019 PyData
35:30 Gianluca Campanella: The unreasonable effectiveness of feature hashing | PyData London 2019 PyData
33:34 Michael Sugimura: Computer Vision and NLP for Multi-Task Fashion Attrb. Modeling |PyData London 2019 PyData
1:17:01 Imran Rashid:Training intelligent game agents using deep reinforcement learning | PyData London 2019 PyData
1:40:18 Jeffrey Hsu, Susannah Klanecek: A Deep Dive into NLP with PyTorch | PyData London 2019 PyData
35:38 Maria Navarro: Quantifying uncertainty in Machine Learning predictions | PyData London 2019 PyData
40:25 Igor Gotlibovych: Deep Learning and Time Series Forecasting for Smarter Energy | PyData London 2019 PyData
