Machine-Learning-Quick-Tour

A collections of lab design for project training.

Environment Setup

Clone the project
docker build -t="ml_training" .
docker run -d -p 8888:8888 -v /home/training/Machine-Learning-Tour-Sklearn:/home/training/notebooks ml_training
Go to localhost:8888, and you should see the notebook running

For windows/MACOS user, be sure to share the folder in the docker setting first

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
.ipynb_checkpoints		.ipynb_checkpoints
assets		assets
fig_code		fig_code
images		images
requirements		requirements
.bashrc.template		.bashrc.template
Appendix-Density Estimation.ipynb		Appendix-Density Estimation.ipynb
Appendix-Random Forest.ipynb		Appendix-Random Forest.ipynb
Appendix-SVM.ipynb		Appendix-SVM.ipynb
Dockerfile		Dockerfile
Lab0-0-Scientific-Computing-with-Python.ipynb		Lab0-0-Scientific-Computing-with-Python.ipynb
Lab0-1-IPythonMagic.ipynb		Lab0-1-IPythonMagic.ipynb
Lab0-2-Quick_Introduction.ipynb		Lab0-2-Quick_Introduction.ipynb
Lab1-Overview.ipynb		Lab1-Overview.ipynb
Lab1-Overview.slides.html		Lab1-Overview.slides.html
Lab2-Classification.ipynb		Lab2-Classification.ipynb
Lab3-Regression.ipynb		Lab3-Regression.ipynb
Lab4-Clustering.ipynb		Lab4-Clustering.ipynb
Lab5-Model Selection.ipynb		Lab5-Model Selection.ipynb
Lab6-Dimension Reduction.ipynb		Lab6-Dimension Reduction.ipynb
README.md		README.md
Tutorial.pptx		Tutorial.pptx
apt-packages.txt		apt-packages.txt
requirements.txt		requirements.txt
run_ipython.sh		run_ipython.sh