Please log in to watch this conference skillscast.
The data science team at HealthUnlocked is used to using mature Python libraries to process text and implement machine learning algorithms. During this talk, you will explore a journey to translate a Python model prototype into Clojure production code.
You will learn how the HU team implemented our natural language processing pipeline, including tokenisation and vectorisation, as well as the core Naive Bayes algorithm, from first principles.
YOU MAY ALSO LIKE:
- Now you're Speaking my Language! : Building, Maintaining and Using a Patient-Friendly Medical Ontology (SkillsCast recorded in December 2018)
- Improving Software Quality through Data with Markus Harrer (Online Workshop on 14th - 15th November 2022)
- TDD and other drugs (Online Meetup on 8th August 2022)
- LJC: I Started Testing In Production... Then I Went On Holiday (Online Meetup on 15th August 2022)
- Keynote — Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation (SkillsCast recorded in November 2021)
- Adopting F# on a Consultancy Project: From Zero to MVP to V0 Launch (SkillsCast recorded in October 2021)
Clojure for Data Science: from a Prototype in Python to Clojure in Production
Chloe Pont
Chloe is currently part of the data science team at HealthUnlocked which aims to improve user experience on the platform and facilitate user data analysis. She has always been passionate about healthcare and psychology and she enjoys digging for new insights in medical data.
Maria Mestre
Maria is the lead data scientist at HealthUnlocked, a health social network. Her main role is to build the data pipelines and machine algorithms that power THE team's content recommender and intelligent content tagger. Before working at HealthUnlocked, she did a PhD in Cambridge in signal processing & machine learning. She then moved to work at a startup using mainly big data tools (Spark) and Python.