Name: Random Forests for Classification - Easy Machine Learning For Everyone
Start: 2017-01-26T18:30:00Z
End: 2017-01-26T21:30:00Z
Location: The Metal Box Factory

We're lucky to have Ian Ozswald introduce Random Forests with Python.

There is huge interest in machine learning and data mining at the moment, but sometimes all the tools and methods can seem a little overwhelming. Luckily it doesn't have to be.

This talk is aimed at developers who want to use machine learning to solve their own binary (2 class) classification task. No prior machine learning or math experience is required. This talk will cover feature engineering (including a robust solution to 'the problem of null data'), predicting the right class with a Random Forest, cross-validating to avoid over-fitting, diagnosing problems in the classifier and approaches to deploying the classifier in the real world.

Ian's goal is to provide you with a process that you can take back to the office to try with your own data, using the popular scikit-learn library. It'll be backed by reproducible working code.

Ian is the author of High Performance Python and an organiser of PyData London.

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We'll also have a flash talk by Yeray Diaz on asyncio.

Tariq Rashid

Simon Harrison

Patrick Morris

London Python

London Python

Random Forests for Classification - Easy Machine Learning For Everyone

London Python

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