Machine Learning in Python, SMU CS5324/CS7324

This is a repository for my course at SMU, CSE5/7324. It should be accessible for anyone with coding experience in Python and some machine learning knowledge. Feel free to use any and all code for any purpose.

Yaml environment file coming soon. Untl then, you can start you own environment with the folliwing installs:

conda create --name mlenv2020 python=3.7 jupyter numpy pandas matplotlib scikit-learn tensorflow plotly scikit-image

If you wish to use JupyterLab, some visualization dependencies need to be installed:

conda create -n mlenv anaconda plotly conda activate mlenv conda install -c conda-forge missingno conda install nodejs jupyter labextension install @jupyterlab/plotly-extension jupyter labextension install @jupyter-widgets/jupyterlab-manager conda update scikit-image conda deactivate

Name		Name	Last commit message	Last commit date
Latest commit History 250 Commits
Flipped Assignments		Flipped Assignments
PDF_Slides		PDF_Slides
data		data
.gitignore		.gitignore
0. Python_Intro (optional).ipynb		0. Python_Intro (optional).ipynb
01_Numpy and Pandas Intro.ipynb		01_Numpy and Pandas Intro.ipynb
02_Document Feature Engineering (optional).ipynb		02_Document Feature Engineering (optional).ipynb
02a_Pandas (optional).ipynb		02a_Pandas (optional).ipynb
03. DataVisualization.ipynb		03. DataVisualization.ipynb
04. Dimension Reduction and Images.ipynb		04. Dimension Reduction and Images.ipynb
05. Logistic Regression.ipynb		05. Logistic Regression.ipynb
06. Optimization.ipynb		06. Optimization.ipynb
07. MLP Neural Networks.ipynb		07. MLP Neural Networks.ipynb
07a. MLP Neural Networks with Bias.ipynb		07a. MLP Neural Networks with Bias.ipynb
08. Practical_NeuralNets.ipynb		08. Practical_NeuralNets.ipynb
08a. Practical_NeuralNets with Bias.ipynb		08a. Practical_NeuralNets with Bias.ipynb
09. Evaluation.ipynb		09. Evaluation.ipynb
10. Keras Wide and Deep.ipynb		10. Keras Wide and Deep.ipynb
10a. Keras Wide and Deep as TFData.ipynb		10a. Keras Wide and Deep as TFData.ipynb
11. Convolutional Neural Networks.ipynb		11. Convolutional Neural Networks.ipynb
12. More Advanced CNN Techniques.ipynb		12. More Advanced CNN Techniques.ipynb
12a. More Advanced CNN Techniques as TFData.ipynb		12a. More Advanced CNN Techniques as TFData.ipynb
13. RNN Basics.ipynb		13. RNN Basics.ipynb
13a. Sequence Basics [experimental].ipynb		13a. Sequence Basics [experimental].ipynb
14. ConceptNet.ipynb		14. ConceptNet.ipynb
Apple M1 and M2 Install.ipynb		Apple M1 and M2 Install.ipynb
README.md		README.md
model.png		model.png

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Machine Learning in Python, SMU CS5324/CS7324

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