Události

Wednesdays 18:00 - 19:30 
Starting date: 13 November 2019
Finishing date: 4 December 2019
Duration: 4 lessons
Course instructor: Ondřej Zahradník

Registration for this course is closed.

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There are many online courses explaining different pieces of ML. Very few of them answer how to really apply ML in production problems. We share some best practices on how to build ML system. All built with simple examples in Python but general enough for non-pythonists too.

This course will cover:

1. Data

Given data it is quite simple to build some models. We go through typical modeling workflow from data exploration, feature engineering, to modeling on a complete worked-out example, discuss options and tradeoffs.

2. ML in Production

We test several models in laboratory conditions. We try to extract some knowledge from the model to help us with stakeholders’ buy-in. We move the best model from messy notebooks into production. We give an overview of techniques used to ease transition from development to production and how to keep the model running well.

3. Experimentation

There is no improvement without failures, we have to know what works and what does not. We give examples of basic techniques to run controlled experiments and learn from them. We help to communicate results in natural language and how to get most of the value from the experiment using Bayesian approach.

4. Deep Learning

Deep learning helps where traditional techniques stop. It does not need to be too difficult and technical to implement. We give an example of a problem solved using deep net, what are common pitfalls and how to evade them.

Participants who attend at least 75% of the sessions can claim a Certificate of Attendance issued by CERGE-EI.

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