Here are the companion notebooks of my training session at the Open Data Science Conference (ODSC) (16th Sept. 2020).
Programming is a great way to get practical insights about math theoretical concepts. The goal of this session is to show you that you can start learning the math needed for machine learning and data science using code. You'll learn about scalars, vectors, matrices and tensors, and see how to use linear algebra on your data.
This content is inspired from the book Essential Math for Data Science that I'm currently writing (estimated date of release: November 2020).
Here are the steps to do to follow the session using the Jupyter notebooks. This will allow you to do the exercises.
First, check that you have Python installed on your computer. Open a terminal and run:
python --version
If Python 3 is installed, it should return, for instance, python 3.7.6. If not, here are the steps to install it:
- Install Brew:
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
- Install Python
brew install python
You can check that Python 3 is now installed:
python --version
Next, you'll install virtual environments. Virtual environments are used to install specific Python libraries (and specific versions) for a project. This avoids conflict if you need different versions of Python or of libraries in two projects.
- Install pipenv:
brew install pipenv
Check that it worked:
pipenv --version
Download the Github repository that contains the notebooks at this address: https://github.com/hadrienj/odsc_training.
Unzip the file, open a terminal and go to that folder (replace with the correct path):
cd path/to/your/repo
Now, you can create a virtual environment with Python 3.7 for the project:
pipenv --python 3.7
Then, dinstall the required libraries:
pipenv install
This line will install all the libraries listed in the file Pipfile.lock.
Finally, you can start the notebook server (from the virtual environment pipenv) as following:
pipenv run jupyter notebook
It should start a web page listing the files in the repository. You can then open the notebook you want (the .ipynb files).