Introduction¶
This course Introduction to Machine Learning with Python was lectured by Avast as part of Economics Discovery Hub at CERGE-EI.
This course covers:
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 trade-offs.
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. Furthermore, we give an overview of techniques used to ease transition from development to production and how to keep the model running well.
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.
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.
Course Materials¶
You can browse notebooks in git see Lesson 1 Notebook. If you want to take the courses in code and play with notebooks, you need to set up your own python and Jupyter notebook environment.