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Code repo for "Create machine learning models", the Data Science Learning Path on Microsoft Learn. This course is based on The textbook Computational and Inferential Thinking: The Foundations of Data Science http://www.inferentialthinking.com

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Fruitymo/learn-data-science-method

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learn-data-science-method

Code repo for create-machine-learn-models, the Data Science Learning Path on Microsoft Learn. This course is based on The University of California, Berkeley - Computational and Inferential Thinking: The Foundations of Data Science textbook http://www.inferentialthinking.com The learning Path only constitutes of code snippets, so here you find code you can run and get the output as indicated. refer to the Jupyter notebook.

4 March 2023 update:

Start by importing urllib3 library, a powerful, user-friendly HTTP client for Python.

To make use of urllib3 library, make sure you pip install using the following shell commands

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python get-pip.py
python -m pip install urllib3

Once urllib3 is installed the import urllib3 should excute without errors.

Running python code in Jupyter notebook vs Python scripts on commandline

Version 1.76 of visual studio code comes with Interactive Window, so for people who are not familiar with working with Jupyter notebooks, they can simply run the python files in visual studio code or straight from command line using the following shell command: python ReadNewOnlineBook.py

20 June 2025 update:

If you get an error running the import urllib3 check first that you are in comand prompt in vscode or windows terminal as PowerShell terminal does not recognize python syntax.

Alternatively, you can create a new ReadBooksOnline.py file, and add your python code in there eg:

import urllib3
# your code here

then run it in terminal: python ReadBooksOnline.py

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Code repo for "Create machine learning models", the Data Science Learning Path on Microsoft Learn. This course is based on The textbook Computational and Inferential Thinking: The Foundations of Data Science http://www.inferentialthinking.com

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