Calling former students . . .

The 3rd Edition of my ML Series is going slower than I'd like. I'm requesting help from former students and the wider community. I've created a folder for code contributions for Volume II: https://drive.google.com/drive/folders/18nQmxfGCis7mglxsrDkt-vmo-MrcHm5c?usp=drive_link

Volume II focuses on machine learning with Sci-Kit Learn. If you have, or would like to create Python notebooks for the following topics, please consider uploading your code. There is no monetary compensation, just bragging rights for your resume and GitHub. If I choose to use your code in Volume II, you will get credit in the book's GitHub as well as in the book pdf. Here are the topics so far:

- Regression using sklearn

- Classification using sklearn

- Either classification or regression: kNN, decision trees, random forests

- SVM in sklearn

- Neural Networks in sklearn

- Ensemble methods in sklearn

- Feature selection in sklearn

- Clustering methods in sklearn

- Preprocessing in sklearn and Python; examples of cleaning data sets

Guidelines:

- document well

- create a .ipynb notebook

- use sklearn, pandas, numpy seaborn or matlab only; future volumes will cover TensorFlow and PyTorch

- test if it runs in colab

This is an experiment, hope it goes well!!!