materials from machine learning lectures in python
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README.md

README.md

README for daml

Description

DAML - Lectures in Data Analytics and Machine Learning

Jupyter notebooks, material, code and data for lectures I give about the practice (and some theory) of Data Science in Python. This repository contains materials that may be presented in a different order, i.e. please do not rely on the repository naming convention for material ordering.

Main References

Text, code and examples that are contained within this repository have been heavily influenced by several other texts. While for my own lectures on Data Analytics and Machine Learning I use the (more-or-less) exact material in this repository, several other materials helped me build it. No single material in modern Data Science can be attributed to the effort of a single person, therefore I want to acknowledge the huge effort made by many researchers, teachers and presenters that allowed me to summarise this work into the shape seen in this repository. The main pieces use by me are:

These are by no means exhaustive, where a different reference is used, or gives extra information to the presented material; the reference is placed directly in the text.

Copying

Copyright (C) 2018 Michal Grochmal

This file is part of daml

Text

The text, including code samples when used as presentation text and not runnable code, is licensed under the Creative Commons Share Alike Non Commercial 4.0 - CC BY-NC-SA 4.0 - license. See the COPYING-TEXT file for the full text of the license or read it as creative commons: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.

Code

The code in this repository and any code samples in the notebooks is licensed under the MIT license. The full text of the license can be found in the COPYING-CODE file. Or read on the open source initiative website: https://opensource.org/licenses/MIT.

Data

The Copyright of the data files in this repository is covered by several Open Data initiatives. Where several different licenses are covered, or similar to, public domain attribution; whilst others are licensed under very specific licenses that allow for non-commercial (or academic only) usage. This repository uses the data in an academic fashion but, in general, I'd advise against using this data for any non-academic purpose.

As of this writing the complications about Open Data policies would require me to track the use of every data provided to each user, which is inviable. Instead I point to the place from where the data can be retrieved from and advise that, if you want to use this data you download it from there.