👾 Machine Learning Recipes

Code developed in video series "Machine Learning Recipes", eventually with some personal comments and annotations.

Installation

# Ubuntu < 16.04
sudo apt-get install libatlas-dev libatlas3-base gfortran python-dev\
    libblas3 liblapack3 build-essential libatlas-base-dev graphviz\
    libgraphviz-dev pkg-config build-essential python-tk tk-dev\
    libpng12-dev curl


# Ubuntu 16.04+
sudo apt install libblas3 libc6 liblapack3 gcc gfortran python-dev\
    libgcc1 libgfortran3 libstdc++6 g++ graphviz build-essential\
    python-tk tk-dev libpng12-dev curl

# After:
pip install -r requirements.txt

# Docker
docker pull cassiobotaro/mlr

Usage

python <example_code>.py

via docker

docker run --rm -v $(pwd):/mlr casiobotaro/mlr python3 mlr/video<number>/<example_code>.py

Contributing

1. Fork it!
2. Create your feature branch: git checkout -b my-new-feature
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin my-new-feature
5. Submit a pull request :D

History

• Hello World - Machine Learning Recipes #1
• Visualizing a Decision Tree - Machine Learning Recipes #2
• What Makes a Good Feature? - Machine Learning Recipes #3
• Let’s Write a Pipeline - Machine Learning Recipes #4
• Writing Our First Classifier - Machine Learning Recipes #5
• Train an Image Classifier with TensorFlow for Poets - Machine Learning Recipes #6
• Classifying Handwritten Digits with TF.Learn - Machine Learning Recipes #7
• Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8
• Intro to Feature Engineering with TensorFlow - Machine Learning Recipes #9

Credits

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