Merge branch 'restructure-project' into develop
|
@ -1,6 +1,6 @@
|
|||
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
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
|
|
84
README.md
|
@ -1,50 +1,64 @@
|
|||
# Workshop: Machine Learning for Beginners
|
||||
# An Introduction to Data Science
|
||||
|
||||
This repository contains the code for the workshop "Machine Learning for
|
||||
Beginners" as presented in various occasions at
|
||||
[WHU - Otto Beisheim School of Management](https://www.whu.edu), such as the
|
||||
[Campus for Supply Chain Management](https://www.campus-for-supply-chain-management-cscm.de/),
|
||||
[IdeaLab](https://www.idealab.io)'s [IdeaHack](http://www.ideahack.io), or
|
||||
within many [executive education](https://ee.whu.edu/) programs.
|
||||
This project is an introductory workshop
|
||||
in **[Data Science <img height="12" style="display: inline-block" src="static/link/to_wiki.png">](https://en.wikipedia.org/wiki/Data_science)**
|
||||
in the programming language **[Python <img height="12" style="display: inline-block" src="static/link/to_py.png">](https://www.python.org/)**.
|
||||
To learn about Python and programming in detail,
|
||||
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.
|
||||
|
||||
|
||||
## Prerequisites
|
||||
### Table of Contents
|
||||
|
||||
To be suitable for *total beginners*, there are *no* prerequisites.
|
||||
If you are interested to learn more after this workshop, check out the
|
||||
full-semester course **[Introduction to Python & Programming](https://github.com/webartifex/intro-to-python)**.
|
||||
- *Chapter 0*: Python in a Nutshell
|
||||
- *Chapter 1*: Python's Scientific Stack
|
||||
- *Chapter 2*: A first Example: Classifying Flowers
|
||||
- *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
|
||||
**Python 3.7** or higher is required.
|
||||
The **main goal** is to **show** students
|
||||
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
|
||||
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.
|
||||
### Prerequisites
|
||||
|
||||
To download the course's materials as a ZIP file, click on the green "Clone or
|
||||
download" button on the top right on this website.
|
||||
Then, unpack the ZIP file into a folder of your choosing (ideally somewhere
|
||||
within your personal user folder so that the files show up right away).
|
||||
To be suitable for *beginners*, there are *no* formal prerequisites.
|
||||
It is only expected that the student has:
|
||||
- a *solid* understanding of the **English** language and
|
||||
- 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
|
||||
|
||||
Alexander Hess is a PhD student at the Chair of Logistics Management at the
|
||||
[WHU - Otto Beisheim School of Management](https://www.whu.edu) where he
|
||||
conducts research on urban delivery platforms and teaches an introductory
|
||||
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),
|
||||
[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).
|
||||
Alexander Hess is a PhD student
|
||||
at the Chair of Logistics Management at [WHU - Otto Beisheim School of Management](https://www.whu.edu)
|
||||
where he conducts research on urban delivery platforms
|
||||
and teaches coding courses based on Python in the BSc and MBA programs.
|
||||
|
||||
Connect with him on [LinkedIn](https://www.linkedin.com/in/webartifex).
|
||||
|
|
1409
poetry.lock
generated
|
@ -1,20 +1,31 @@
|
|||
[build-system]
|
||||
requires = ["poetry>=0.12"]
|
||||
build-backend = "poetry.masonry.api"
|
||||
requires = ["poetry-core>=1.0.0"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.poetry]
|
||||
name = "workshop-machine-learning-for-beginners"
|
||||
version = "0.1.0"
|
||||
name = "intro-to-data-science"
|
||||
version = "0.1.0.dev0"
|
||||
|
||||
authors = ["Alexander Hess <alexander@webartifex.biz>"]
|
||||
description = "An introductory workshop on machine learning"
|
||||
authors = [
|
||||
"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"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.7"
|
||||
readme = "README.md"
|
||||
homepage = "https://github.com/webartifex/intro-to-data-science"
|
||||
repository = "https://github.com/webartifex/intro-to-data-science"
|
||||
|
||||
jupyterlab = "^2.2.8"
|
||||
matplotlib = "^3.3.2"
|
||||
numpy = "^1.19.2"
|
||||
pandas = "^1.1.2"
|
||||
scikit-learn = "^0.23.2"
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.8"
|
||||
|
||||
[tool.poetry.dev-dependencies]
|
||||
|
|
Before Width: | Height: | Size: 33 KiB |
Before Width: | Height: | Size: 55 KiB |
BIN
raw/examples.png
Before Width: | Height: | Size: 344 KiB |
Before Width: | Height: | Size: 32 KiB |
BIN
raw/iris.png
Before Width: | Height: | Size: 21 KiB |
Before Width: | Height: | Size: 497 KiB |
BIN
raw/knn.png
Before Width: | Height: | Size: 76 KiB |
Before Width: | Height: | Size: 182 KiB |
Before Width: | Height: | Size: 113 KiB |
BIN
raw/r.png
Before Width: | Height: | Size: 76 KiB |
BIN
raw/spam.png
Before Width: | Height: | Size: 96 KiB |
Before Width: | Height: | Size: 73 KiB |
|
@ -1,56 +0,0 @@
|
|||
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
|
||||
ipython-genutils==0.2.0
|
||||
ipywidgets==7.5.1
|
||||
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
|
||||
kiwisolver==1.1.0
|
||||
MarkupSafe==1.1.1
|
||||
matplotlib==3.1.2
|
||||
mistune==0.8.4
|
||||
more-itertools==8.0.0
|
||||
nbconvert==5.6.1
|
||||
nbformat==4.4.0
|
||||
notebook==6.0.2
|
||||
numpy==1.17.4
|
||||
pandas==0.25.3
|
||||
pandocfilters==1.4.2
|
||||
parso==0.5.1
|
||||
pexpect==4.7.0
|
||||
pickleshare==0.7.5
|
||||
prometheus-client==0.7.1
|
||||
prompt-toolkit==2.0.10
|
||||
ptyprocess==0.6.0
|
||||
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
|
2
static/link/README.md
Normal file
|
@ -0,0 +1,2 @@
|
|||
This folder contains small images
|
||||
that are used to enhance the links in the notebooks and markdown files.
|
BIN
static/link/to_gh.png
Normal file
After Width: | Height: | Size: 1.2 KiB |
BIN
static/link/to_py.png
Normal file
After Width: | Height: | Size: 1.3 KiB |
BIN
static/link/to_wiki.png
Normal file
After Width: | Height: | Size: 503 B |