This GitHub respository contains all of the Google Colab materials from the first run of MSSP 608 - Practical Machine Learning. The Google Colab links to all the notebooks will be updated here and the Lecture/Homework notebook sources will be uploaded here in folders.
- Day 1 [Exploratory Data Analysis, Decision Trees, Information Theory, and Intro to Machine Learning]
- Day 2 [Test Sets, Metrics, and Logistic Regression]
- Day 3 [Cross Validation and Hyperparameters]
- Day 4 [Text Data and Naive Bayes]
- Day 5 [Dependency Parsing, Multilingual NLP. Domain Transfer, and Word Embeddings]
- Day 6 [k-Nearest-Neighbors and Support Vector Machines]
- Day 7 [Ensemble Methods and Unsupervised Learning]
- Day 8 [Fairness and Explainability]
- Day 9 [Neural Networks and Deep Learning]
- Recitation #2 - Python Libraries, Hyperparameter Optimization, Bias/Variance
- Recitation #3 - SVMs, Naive Bayes, kNNs, and tricks in Google Colab
- Recitation #4 - Ensembles and Unsupervised Learning
- Bias and Fairness
- Recitation #5 - Deep Learning
- Recitation #6 - Personal Websites
First open the link to whatever notebook you want to run (you must also be signed into a Google Account). Then follow the link that says "Open in Playgroud"
After this, simply navigate to the run tab and click "run all" now you have a fully run, completely editable jupyter notebook running in the cloud! Yay!
If you get a warning, just hit "Run Anyways".
For more info on Google Colab, check out this tutorial from Google