alexander/intro-to-data-science
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- make it more generic (not cscm in the names)
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LICENSE
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MIT License
Copyright (c) 2018 Alexander Hess [alexander@webartifex.biz]
Copyright (c) 2018-2019 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

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README.md
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# Introduction to Machine Learning with Python
# Workshop: Machine Learning for Beginners
## General Notes
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 contains a Jupyter notebook introducing some very basic concepts
of machine learning and the popular Iris classification case study.
First, some simple linear algebra ideas are shown via examples with the numpy
library. Then a so-called K-nearest-neighbor algorithm is trained to classify
flower from the Iris dataset.
## Prerequisites
This intro is aimed at total beginners to programming and machine learning.
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)**.
It was used within a 90 minute workshop at the
[WHU Campus for Supply Chain Management](http://campus-for-scm.de), which
targets students of business administration and young management professionals.
## Installation
This project uses popular Python libraries that can be installed via the
pipenv command line tool. To do so, run `pipenv install` or
`pipenv install --ignore-pipfile` (to use the exact environment as of the time
of this writing). For a tutorial on pipenv, go to the official
[documentation](https://pipenv.readthedocs.io/en/latest/).
To follow this workshop on your own computer, a working installation of
**Python 3.6** or higher is required.
After installation, start Jupyter via the command `jupyter notebook` and wait
for a new tab to be opened in your default web browser. Then, open the notebook
called [intro_to_machine_learning.ipynb](intro_to_machine_learning.ipynb).
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., *2019-10* with Python 3.7 at the
time of this writing) for your operating system.
## Read-only Version
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.
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).
## 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).
For those interested in just reading the example codes without installing
anything, just open this [notebook](intro_to_machine_learning.ipynb) and view
the Jupyter notebook in your browser.

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pandocfilters = ["b3dd70e169bb5449e6bc6ff96aea89c5eea8c5f6ab5e207fc2f521a2cf4a0da9"]
parso = ["63854233e1fadb5da97f2744b6b24346d2750b85965e7e399bec1620232797dc", "666b0ee4a7a1220f65d367617f2cd3ffddff3e205f3f16a0284df30e774c2a9c"]
pexpect = ["2094eefdfcf37a1fdbfb9aa090862c1a4878e5c7e0e7e7088bdb511c558e5cd1", "9e2c1fd0e6ee3a49b28f95d4b33bc389c89b20af6a1255906e90ff1262ce62eb"]
pickleshare = ["87683d47965c1da65cdacaf31c8441d12b8044cdec9aca500cd78fc2c683afca", "9649af414d74d4df115d5d718f82acb59c9d418196b7b4290ed47a12ce62df56"]
prometheus-client = ["71cd24a2b3eb335cb800c7159f423df1bd4dcd5171b234be15e3f31ec9f622da"]
prompt-toolkit = ["11adf3389a996a6d45cc277580d0d53e8a5afd281d0c9ec71b28e6f121463780", "2519ad1d8038fd5fc8e770362237ad0364d16a7650fb5724af6997ed5515e3c1", "977c6583ae813a37dc1c2e1b715892461fcbdaa57f6fc62f33a528c4886c8f55"]
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pyrsistent = ["34b47fa169d6006b32e99d4b3c4031f155e6e68ebcc107d6454852e8e0ee6533"]
python-dateutil = ["7e6584c74aeed623791615e26efd690f29817a27c73085b78e4bad02493df2fb", "c89805f6f4d64db21ed966fda138f8a5ed7a4fdbc1a8ee329ce1b74e3c74da9e"]
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qtconsole = ["40d5d8e00d070ea266dbf6f0da74c4b9597b8b8d67cd8233c3ffd8debf923703", "b91e7412587e6cfe1644696538f73baf5611e837be5406633218443b2827c6d9"]
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pygments = ["2a3fe295e54a20164a9df49c75fa58526d3be48e14aceba6d6b1e8ac0bfd6f1b", "98c8aa5a9f778fcd1026a17361ddaf7330d1b7c62ae97c3bb0ae73e0b9b6b0fe"]
pyparsing = ["20f995ecd72f2a1f4bf6b072b63b22e2eb457836601e76d6e5dfcd75436acc1f", "4ca62001be367f01bd3e92ecbb79070272a9d4964dce6a48a82ff0b8bc7e683a"]
pyrsistent = ["f3b280d030afb652f79d67c5586157c5c1355c9a58dfc7940566e28d28f3df1b"]
python-dateutil = ["73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c", "75bb3f31ea686f1197762692a9ee6a7550b59fc6ca3a1f4b5d7e32fb98e2da2a"]
pytz = ["1c557d7d0e871de1f5ccd5833f60fb2550652da6be2693c1e02300743d21500d", "b02c06db6cf09c12dd25137e563b31700d3b80fcc4ad23abb7a315f2789819be"]
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pywinpty = ["1e525a4de05e72016a7af27836d512db67d06a015aeaf2fa0180f8e6a039b3c2", "2740eeeb59297593a0d3f762269b01d0285c1b829d6827445fcd348fb47f7e70", "2d7e9c881638a72ffdca3f5417dd1563b60f603e1b43e5895674c2a1b01f95a0", "33df97f79843b2b8b8bc5c7aaf54adec08cc1bae94ee99dfb1a93c7a67704d95", "5fb2c6c6819491b216f78acc2c521b9df21e0f53b9a399d58a5c151a3c4e2a2d", "8fc5019ff3efb4f13708bd3b5ad327589c1a554cb516d792527361525a7cb78c", "b358cb552c0f6baf790de375fab96524a0498c9df83489b8c23f7f08795e966b", "dbd838de92de1d4ebf0dce9d4d5e4fc38d0b7b1de837947a18b57a882f219139", "dd22c8efacf600730abe4a46c1388355ce0d4ab75dc79b15d23a7bd87bf05b48", "e854211df55d107f0edfda8a80b39dfc87015bef52a8fe6594eb379240d81df2"]
pyzmq = ["01b588911714a6696283de3904f564c550c9e12e8b4995e173f1011755e01086", "0573b9790aa26faff33fba40f25763657271d26f64bffb55a957a3d4165d6098", "0fa82b9fc3334478be95a5566f35f23109f763d1669bb762e3871a8fa2a4a037", "1e59b7b19396f26e360f41411a5d4603356d18871049cd7790f1a7d18f65fb2c", "2a294b4f44201bb21acc2c1a17ff87fbe57b82060b10ddb00ac03e57f3d7fcfa", "355b38d7dd6f884b8ee9771f59036bcd178d98539680c4f87e7ceb2c6fd057b6", "4b73d20aec63933bbda7957e30add233289d86d92a0bb9feb3f4746376f33527", "4ec47f2b50bdb97df58f1697470e5c58c3c5109289a623e30baf293481ff0166", "5541dc8cad3a8486d58bbed076cb113b65b5dd6b91eb94fb3e38a3d1d3022f20", "6fca7d11310430e751f9832257866a122edf9d7b635305c5d8c51f74a5174d3d", "7369656f89878455a5bcd5d56ca961884f5d096268f71c0750fc33d6732a25e5", "75d73ee7ca4b289a2a2dfe0e6bd8f854979fc13b3fe4ebc19381be3b04e37a4a", "80c928d5adcfa12346b08d31360988d843b54b94154575cccd628f1fe91446bc", "83ce18b133dc7e6789f64cb994e7376c5aa6b4aeced993048bf1d7f9a0fe6d3a", "8b8498ceee33a7023deb2f3db907ca41d6940321e282297327a9be41e3983792", "8c69a6cbfa94da29a34f6b16193e7c15f5d3220cb772d6d17425ff3faa063a6d", "8ff946b20d13a99dc5c21cb76f4b8b253eeddf3eceab4218df8825b0c65ab23d", "972d723a36ab6a60b7806faa5c18aa3c080b7d046c407e816a1d8673989e2485", "a6c9c42bbdba3f9c73aedbb7671815af1943ae8073e532c2b66efb72f39f4165", "aa3872f2ebfc5f9692ef8957fe69abe92d905a029c0608e45ebfcd451ad30ab5", "cf08435b14684f7f2ca2df32c9df38a79cdc17c20dc461927789216cb43d8363", "d30db4566177a6205ed1badb8dbbac3c043e91b12a2db5ef9171b318c5641b75", "d5ac84f38575a601ab20c1878818ffe0d09eb51d6cb8511b636da46d0fd8949a", "e37f22eb4bfbf69cd462c7000616e03b0cdc1b65f2d99334acad36ea0e4ddf6b", "e6549dd80de7b23b637f586217a4280facd14ac01e9410a037a13854a6977299", "ed6205ca0de035f252baa0fd26fdd2bc8a8f633f92f89ca866fd423ff26c6f25", "efdde21febb9b5d7a8e0b87ea2549d7e00fda1936459cfb27fb6fca0c36af6c1", "f4e72646bfe79ff3adbf1314906bbd2d67ef9ccc71a3a98b8b2ccbcca0ab7bec"]
qtconsole = ["4de25b8895957d23ceacf2526b6f0a76da4e60e60115611930d387c853f3cb08", "654f423662e7dfe6a9b26fac8ec76aedcf742c339909ac49f1f0c1a1b744bcd1"]
scikit-learn = ["0098757148ee055796370ca5f4c5887940c46f87a4989f7ca9be6a2c42803ef1", "06b78e6f62b6a89b00acc873ee823c99ddf4ee1d461a02ce0d22276a17d2c13e", "07aaa1d639759ebfa33e747022d3fde880eb4343c6a7ddd916478be3a6b98d67", "087fffad9e7604bbbaa078bdfdf6919a96495f0eb742c70dd900820224c20a0a", "09b81c1145437fd5d25a2e8419621185c22b05450a7c77ad0a568194bbd65963", "1632967d8fbae09e6090ef6bd632681c5fc64b95378a858c59fd37b57357425e", "3004fe60aca1f20b80d13698e5d9123e0d500062b548c733a9f230ab943ce334", "314abf60c073c48a1e95feaae9f3ca47a2139bd77cebb5b877c23a45c9e03012", "594e693aef1dca29ab5823781f8db15815f257295cff52868f0602553ee5c66b", "5e426ed57851e60d2edb63a60888cc85e47b129f69f9c26eb872d8b7581c4c63", "63b7c4ddd5a6ed504ee7a6d2670dc8df478b70c4e31a2d165de82c4d6f4b6e1b", "8049f6330bbd1f8dd8db587fbfb69f8150efb36a22ddb4d178a0479c027496c5", "8509da5e03155c872d2e646763f4d42cfbdbd460dad9b803dba7602c32b7a605", "8c524b4567bb4d5ea172aa0d8212fe1b06898c4ad130ac443bbe0e5f4bd9d104", "ab3f791d5663bcc8137ea2339cbbd81907d2c7f51da6ef0402a6a37ef74bd857", "ac81facbda6ac2296e5d7b7518dc15d93858fda34f7d7877a5e9bbc2c8b0b5aa", "bc48a36424a6af3c353827a5d68abdad132f5ca843d721852fdf8b2e8d6277d3", "c252cfb331e15188d731253cffaa04a87fb0ea7aad5bff9f85229b5b883c8290", "ca60076ba9e38ed936a0e7fb5a0d18cffe375840d9dc4e562df7e0f5ee066d4d", "df3111e9a6d1b5009b45d10e98276e1e7fafefc538a6496e4e80042bba27cf68", "e321baa1210d20ac9751f4f8ec5e64affc44c93992a7e61611663884cd3e4b5a"]
scipy = ["0b8c9dc042b9a47912b18b036b4844029384a5b8d89b64a4901ac3e06876e5f6", "18ad034be955df046b5a27924cdb3db0e8e1d76aaa22c635403fe7aee17f1482", "225d0b5e140bb66df23d438c7b535303ce8e533f94454f4e5bde5f8d109103ea", "2f690ba68ed7caa7c30b6dc48c1deed22c78f3840fa4736083ef4f2bd8baa19e", "4b8746f4a755bdb2eeb39d6e253a60481e165cfd74fdfb54d27394bd2c9ec8ac", "4ba2ce1a58fe117e993cf316a149cf9926c7c5000c0cdc4bc7c56ae8325612f6", "546f0dc020b155b8711159d53c87b36591d31f3327c47974a4fb6b50d91589c2", "583f2ccd6a112656c9feb2345761d2b19e9213a094cfced4e7d2c1cae4173272", "64bf4e8ae0db2d42b58477817f648d81e77f0b381d0ea4427385bba3f959380a", "7be424ee09bed7ced36c9457f99c826ce199fd0c0f5b272cf3d098ff7b29e3ae", "869465c7ff89fc0a1e2ea1642b0c65f1b3c05030f3a4c0d53d6a57b2dba7c242", "884e619821f47eccd42979488d10fa1e15dbe9f3b7660b1c8c928d203bd3c1a3", "a42b0d02150ef4747e225c31c976a304de5dc8202ec35a27111b7bb8176e5f13", "a70308bb065562afb936c963780deab359966d71ab4f230368b154dde3136ea4", "b01ea5e4cf95a93dc335089f8fbe97852f56fdb74afff238cbdf09793103b6b7", "b7b8cf45f9a48f23084f19deb9384a1cccb5e92fbc879b12f97dc4d56fb2eb92", "bb0899d3f8b9fe8ef95b79210cf0deb6709542889fadaa438eeb3a28001e09e7", "c008f1b58f99f1d1cc546957b3effe448365e0a217df1f1894e358906e91edad", "cfee99d085d562a7e3c4afe51ac1fe9b434363489e565a130459307f30077973", "dfcb0f0a2d8e958611e0b56536285bb435f03746b6feac0e29f045f7c6caf164", "f5d47351aeb1cb6bda14a8908e56648926a6b2d714f89717c71f7ada41282141"]
send2trash = ["60001cc07d707fe247c94f74ca6ac0d3255aabcb930529690897ca2a39db28b2", "f1691922577b6fa12821234aeb57599d887c4900b9ca537948d2dac34aea888b"]
six = ["3350809f0555b11f552448330d0b52d5f24c91a322ea4a15ef22629740f3761c", "d16a0141ec1a18405cd4ce8b4613101da75da0e9a7aec5bdd4fa804d0e0eba73"]
terminado = ["d9d012de63acb8223ac969c17c3043337c2fcfd28f3aea1ee429b345d01ef460", "de08e141f83c3a0798b050ecb097ab6259c3f0331b2f7b7750c9075ced2c20c2"]
testpath = ["46c89ebb683f473ffe2aab0ed9f12581d4d078308a3cb3765d79c6b2317b0109", "b694b3d9288dbd81685c5d2e7140b81365d46c29f5db4bc659de5aa6b98780f8"]
six = ["1f1b7d42e254082a9db6279deae68afb421ceba6158efa6131de7b3003ee93fd", "30f610279e8b2578cab6db20741130331735c781b56053c59c4076da27f06b66"]
terminado = ["4804a774f802306a7d9af7322193c5390f1da0abb429e082a10ef1d46e6fb2c2", "a43dcb3e353bc680dd0783b1d9c3fc28d529f190bc54ba9a229f72fe6e7a54d7"]
testpath = ["60e0a3261c149755f4399a1fff7d37523179a70fdc3abdf78de9fc2604aeec7e", "bfcf9411ef4bf3db7579063e0546938b1edda3d69f4e1fb8756991f5951f85d4"]
tornado = ["349884248c36801afa19e342a77cc4458caca694b0eda633f5878e458a44cb2c", "398e0d35e086ba38a0427c3b37f4337327231942e731edaa6e9fd1865bbd6f60", "4e73ef678b1a859f0cb29e1d895526a20ea64b5ffd510a2307b5998c7df24281", "559bce3d31484b665259f50cd94c5c28b961b09315ccd838f284687245f416e5", "abbe53a39734ef4aba061fca54e30c6b4639d3e1f59653f0da37a0003de148c7", "c845db36ba616912074c5b1ee897f8e0124df269468f25e4fe21fe72f6edd7a9", "c9399267c926a4e7c418baa5cbe91c7d1cf362d505a1ef898fde44a07c9dd8a5"]
traitlets = ["9c4bd2d267b7153df9152698efb1050a5d84982d3384a37b2c1f7723ba3e7835", "c6cb5e6f57c5a9bdaa40fa71ce7b4af30298fbab9ece9815b5d995ab6217c7d9"]
traitlets = ["70b4c6a1d9019d7b4f6846832288f86998aa3b9207c6821f3578a6a6a467fe44", "d023ee369ddd2763310e4c3eae1ff649689440d4ae59d7485eb4cfbbe3e359f7"]
wcwidth = ["3df37372226d6e63e1b1e1eda15c594bca98a22d33a23832a90998faa96bc65e", "f4ebe71925af7b40a864553f761ed559b43544f8f71746c2d756c7fe788ade7c"]
webencodings = ["a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", "b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"]
widgetsnbextension = ["079f87d87270bce047512400efd70238820751a11d2d8cb137a5a5bdbaf255c7", "bd314f8ceb488571a5ffea6cc5b9fc6cba0adaf88a9d2386b93a489751938bcd"]
zipp = ["3718b1cbcd963c7d4c5511a8240812904164b7f381b647143a89d3b98f9bcd8e", "f06903e9f1f43b12d371004b4ac7b06ab39a44adc747266928ae6debfa7b3335"]

6
pyproject.toml
View file

@ -1,7 +1,7 @@
[tool.poetry]
name = "cscm-workshop-intro-machine-learning"
name = "workshop-machine-learning-for-beginners"
version = "0.1.0"
description = "An introductory workshop on machine learning for WHU's Campus for Supply Chain Management"
description = "An introductory workshop on machine learning"
authors = ["Alexander Hess <alexander@webartifex.biz>"]
license = "MIT"
@ -11,7 +11,7 @@ jupyter = "^1.0"
numpy = "^1.17"
matplotlib = "^3.1"
pandas = "^0.25"
scikit-learn = "^0.21"
scikit-learn = "^0.22"
[tool.poetry.dev-dependencies]

57
requirements.txt
View file

@ -1,53 +1,56 @@
attrs==19.2.0
attrs==19.3.0
backcall==0.1.0
bleach==3.1.0
cycler==0.10.0
decorator==4.4.0
decorator==4.4.1
defusedxml==0.6.0
entrypoints==0.3
ipykernel==5.1.2
ipython==7.8.0
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==2.10.1
Jinja2==2.10.3
joblib==0.14.0
jsonschema==3.0.2
jsonschema==3.2.0
jupyter==1.0.0
jupyter-client==5.3.3
jupyter-client==5.3.4
jupyter-console==6.0.0
jupyter-core==4.5.0
jupyter-core==4.6.1
kiwisolver==1.1.0
MarkupSafe==1.1.1
matplotlib==3.1.1
matplotlib==3.1.2
mistune==0.8.4
nbconvert==5.6.0
more-itertools==8.0.0
nbconvert==5.6.1
nbformat==4.4.0
notebook==6.0.1
numpy==1.17.2
pandas==0.25.1
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==2.0.9
prompt-toolkit==2.0.10
ptyprocess==0.6.0
Pygments==2.4.2
pyparsing==2.4.2
pyrsistent==0.15.4
python-dateutil==2.8.0
pytz==2019.2
pyzmq==18.1.0
qtconsole==4.5.5
scikit-learn==0.21.3
scipy==1.3.1
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==1.12.0
terminado==0.8.2
testpath==0.4.2
six==1.13.0
terminado==0.8.3
testpath==0.4.4
tornado==6.0.3
traitlets==4.3.2
traitlets==4.3.3
wcwidth==0.1.7
webencodings==0.5.1
widgetsnbextension==3.5.1
zipp==0.6.0

0
intro_to_machine_learning.ipynb → workshop.ipynb
View file