Practical examples from Machine Learning A-Z rewriten using Jupyter Notebooks.

NOTE This source code does not affilated by SuperDataScience Team (original course authors)

I use macOS as a host system and pyenv to install Python. I recommend to use the latest Python 3.x. There is a trick to make matplotlib works properly: you have to install Python as a framework for macOS. I also suggest you to install all libs globally (without virtualenv) to do not repeat all this tricks again for each virtual environments.

# Install pyenv using Homebrew
brew install pyenv
# Install python 3 as a framework
PYTHON_CONFIGURE_OPTS="--enable-framework" pyenv install 3.6.1
# I always use latest python as global, but you can use it as local
pyenv global 3.6.1
# Ensure you the version, restart your shell otherwise
python --version
# Install all you need for Machine Learning course
pip install jupyter numpy pandas matplotlib sklearn statsmodels

Alternatively you could install python using Homebrew brew install python3 and install all libraries pip3 install jupyter numpy pandas matplotlib sklearn statsmodels.

Get source code and run Jupyter Notebook

# Clone repo to get a working copy
git clone [email protected]:satyrius/machine-learning-az.git
# Change directory
cd machine-learning-az
# Run Notebook
jupyter-notebook

A: This is the best ML course I ever seen. Authors did a great job, they make complex things simple by giving a no-bulshit explanation and giving a lot of real-life practical examples.

A: Because I personnaly don't like Anaconda fat pack and Spider IDE ( ╯°□°)╯ ┻━━┻.