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https://github.com/yandexdataschool/mlatimperial2017

Materials for the course of machine learning at Imperial College organized by Yandex SDA
https://github.com/yandexdataschool/mlatimperial2017

deep-learning imperial-college keras lectures machine-learning practice scikit-learn sklearn theano yandex

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Materials for the course of machine learning at Imperial College organized by Yandex SDA

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# Machine Learning, Imperial College London 2017

[![Join the chat at https://gitter.im/MLatImperial2017/Lobby](https://badges.gitter.im/MLatImperial2017/Lobby.svg)](https://gitter.im/MLatImperial2017/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[![run at everware](https://img.shields.io/badge/run%[email protected]?style=flat)](https://everware.rep.school.yandex.net/hub/oauth_login?repourl=https://github.com/yandexdataschool/MLatImperial2017.git)

A two-weeks in-depth course of machine learning organized by [Yandex Data School](https://yandexdataschool.com) at Imperial College. Contains theory and **much** practice!

Main topics:
- python, scientific python (numpy, scipy, matplotlib)
- python for data science (pandas, sklearn)
- metric models
- linear models
- tree-based models and ensembles, in particular boosting
- dimensionality reduction
- tensor computations and neural networks (theano and keras)

## Challenges

There were two challenges during the course:

- restaraunt reviews classification
- flavour tagging of B mesons