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MIT License MIT License
Copyright (c) 2018-2020 Alexander Hess [alexander@webartifex.biz] Copyright (c) 2018-2021 Alexander Hess [alexander@webartifex.biz]
Permission is hereby granted, free of charge, to any person obtaining a copy Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal of this software and associated documentation files (the "Software"), to deal

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# Workshop: Machine Learning for Beginners # An Introduction to Data Science
This repository contains the code for the workshop "Machine Learning for This project is an introductory workshop
Beginners" as presented in various occasions at in **[Data Science <img height="12" style="display: inline-block" src="static/link/to_wiki.png">](https://en.wikipedia.org/wiki/Data_science)**
[WHU - Otto Beisheim School of Management](https://www.whu.edu), such as the in the programming language **[Python <img height="12" style="display: inline-block" src="static/link/to_py.png">](https://www.python.org/)**.
[Campus for Supply Chain Management](https://www.campus-for-supply-chain-management-cscm.de/), To learn about Python and programming in detail,
[IdeaLab](https://www.idealab.io)'s [IdeaHack](http://www.ideahack.io), or this [introductory course <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/webartifex/intro-to-python) is recommended.
within many [executive education](https://ee.whu.edu/) programs.
## Prerequisites ### Table of Contents
To be suitable for *total beginners*, there are *no* prerequisites. - *Chapter 0*: [Python in a Nutshell](00_python_in_a_nutshell.ipynb)
If you are interested to learn more after this workshop, check out the - *Chapter 1*: [Python's Scientific Stack](01_scientific_stack.ipynb)
full-semester course **[Introduction to Python & Programming](https://github.com/webartifex/intro-to-python)**. - *Chapter 2*: [A first Example: Classifying Flowers](02_a_first_example.ipynb)
- *Chapter 3*: [Case Study: House Prices in Ames, Iowa <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/webartifex/ames-housing)
## Installation ### Objective
To follow this workshop on your own computer, a working installation of The **main goal** is to **show** students
**Python 3.7** or higher is required. how **Python** can be used to solve typical **data science** tasks.
A popular and beginner friendly way is to install the [Anaconda Distribution](https://www.anaconda.com/distribution/)
that not only ships Python but comes pre-packaged with a lot of third-party
libraries from the so-called "scientific stack".
Just go to the [download](https://www.anaconda.com/distribution/#download-section)
section and install the latest version (i.e., *2020-02* with Python 3.7 at the
time of this writing) for your operating system.
Then, among others, you will find an entry "Jupyter Notebook" in your start ### Prerequisites
menu.
Click on it and a new tab in your web browser will open where you can switch
between folders as you could in your computer's default file browser.
To download the course's materials as a ZIP file, click on the green "Clone or To be suitable for *beginners*, there are *no* formal prerequisites.
download" button on the top right on this website. It is only expected that the student has:
Then, unpack the ZIP file into a folder of your choosing (ideally somewhere - a *solid* understanding of the **English** language and
within your personal user folder so that the files show up right away). - knowledge of **basic mathematics** from high school.
### Getting started & Installation
To follow this workshop, an installation of **Python 3.8** or higher is expected.
A popular and beginner friendly way is
to install the [Anaconda Distribution](https://www.anaconda.com/products/individual)
that not only ships Python itself
but also comes pre-packaged with a lot of third-party libraries
including [Python's scientific stack](https://scipy.org/about.html).
Detailed instructions can be found [here <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/webartifex/intro-to-python#installation).
## Contributing
Feedback **is highly encouraged** and will be incorporated.
Open an issue in the [issues tracker <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/webartifex/intro-to-data-science/issues)
or initiate a [pull request <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://help.github.com/en/articles/about-pull-requests)
if you are familiar with the concept.
Simple issues that *anyone* can **help fix** are, for example,
**spelling mistakes** or **broken links**.
If you feel that some topic is missing entirely, you may also mention that.
The materials here are considered a **permanent work-in-progress**.
## About the Author ## About the Author
Alexander Hess is a PhD student at the Chair of Logistics Management at the Alexander Hess is a PhD student
[WHU - Otto Beisheim School of Management](https://www.whu.edu) where he at the Chair of Logistics Management at [WHU - Otto Beisheim School of Management](https://www.whu.edu)
conducts research on urban delivery platforms and teaches an introductory where he conducts research on urban delivery platforms
course on Python (cf., [Fall Term 2019](https://vlv.whu.edu/campus/all/event.asp?objgguid=0xE57C2715B01B441AAFD3E79AA05CACCF&from=vvz&gguid=0x6A2B0ED5B2B949E69957A2099E7DE2F1&mode=own&tguid=0x3980A9BBC3BF4A638E977F2DC163F44B&lang=en), and teaches coding courses based on Python in the BSc and MBA programs.
[Spring Term 2020](https://vlv.whu.edu/campus/all/event.asp?objgguid=0x3354F4C108FF4E959CDD692A325D9AFE&from=vvz&gguid=0x262E29795DD742CFBDE72B12B69CEFD6&mode=own&lang=en&tguid=0x2E4A7D1FF3C34AD08FF07685461781C9)).
Connect him on [LinkedIn](https://www.linkedin.com/in/webartifex).
Connect with him on [LinkedIn](https://www.linkedin.com/in/webartifex).

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[build-system] [build-system]
requires = ["poetry>=0.12"] requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.masonry.api" build-backend = "poetry.core.masonry.api"
[tool.poetry] [tool.poetry]
name = "workshop-machine-learning-for-beginners" name = "intro-to-data-science"
version = "0.1.0" version = "0.1.0"
authors = ["Alexander Hess <alexander@webartifex.biz>"] authors = [
description = "An introductory workshop on machine learning" "Alexander Hess <alexander@webartifex.biz>",
]
description = "An intro to data science for absolute beginners"
keywords = [
"python",
"data-science",
"machine-learning",
"matplotlib",
"numpy",
"seaborn",
"sklearn",
]
license = "MIT" license = "MIT"
[tool.poetry.dependencies] readme = "README.md"
python = "^3.7" homepage = "https://github.com/webartifex/intro-to-data-science"
repository = "https://github.com/webartifex/intro-to-data-science"
jupyterlab = "^2.2.8" [tool.poetry.dependencies]
matplotlib = "^3.3.2" python = "^3.8"
numpy = "^1.19.2" jupyterlab = "^3.0.16"
pandas = "^1.1.2" matplotlib = "^3.4.2"
scikit-learn = "^0.23.2" numpy = "^1.20.3"
pandas = "^1.2.4"
scikit-learn = "^0.24.2"
[tool.poetry.dev-dependencies]

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attrs==19.3.0
backcall==0.1.0
bleach==3.1.0
cycler==0.10.0
decorator==4.4.1
defusedxml==0.6.0
entrypoints==0.3
importlib-metadata==1.2.0
ipykernel==5.1.3
ipython==7.10.1
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jedi==0.15.1
Jinja2==2.10.3
joblib==0.14.0
jsonschema==3.2.0
jupyter==1.0.0
jupyter-client==5.3.4
jupyter-console==6.0.0
jupyter-core==4.6.1
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MarkupSafe==1.1.1
matplotlib==3.1.2
mistune==0.8.4
more-itertools==8.0.0
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notebook==6.0.2
numpy==1.17.4
pandas==0.25.3
pandocfilters==1.4.2
parso==0.5.1
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prometheus-client==0.7.1
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Pygments==2.5.2
pyparsing==2.4.5
pyrsistent==0.15.6
python-dateutil==2.8.1
pytz==2019.3
pyzmq==18.1.1
qtconsole==4.6.0
scikit-learn==0.22
scipy==1.3.3
Send2Trash==1.5.0
six==1.13.0
terminado==0.8.3
testpath==0.4.4
tornado==6.0.3
traitlets==4.3.3
wcwidth==0.1.7
webencodings==0.5.1
widgetsnbextension==3.5.1
zipp==0.6.0

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