From 1d0bba325cc83856f79900462696f2f7705273c0 Mon Sep 17 00:00:00 2001 From: Soh Ohara Date: Tue, 24 May 2022 11:13:16 +0900 Subject: [PATCH] Added Studio Lab button at REDME.md --- README.md | 59 +++++++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 53 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 9ab9ced..09a5d6c 100644 --- a/README.md +++ b/README.md @@ -10,12 +10,59 @@ Watch all class video recordings in this [YouTube playlist](https://www.youtube. ## Course Overview There are five lectures, one final project and five assignments for this class. -| Lecture 1 | Lecture 2 | Lecture 3 | Lecture 4 | Lecture 5 | -| :---: | :---: | :---: | :---: | :---: | -| Decision Trees | Bias-variance trade-off | Bootstrapping | Random Forest Proximities | Boosting | -| Impurity Functions | Error Decomposition | Bagging | Some use cases for Proximities | Gradient Boosting | -| CART Algorithm | Extra Trees Algorithm | Random Forests | Feature Importance in Trees | XGBoost, LightGBM and CatBoost | -| Regularization | Bias-variance and Randomized Ensembles | | Feature Importance in Random Forests | | + +Lecture 1 + +| title | studio lab | +| :---: | ---: | +| Decision Trees | - | +| Impurity Functions | - | +| CART Algorithm | - | +| Regularization | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_1/DTE-LECTURE-1-PRUNE.ipynb)| + +Lecture 2 + +| title | studio lab | +| :---: | ---: | +| Bias-variance trade-off | - | +| Error Decomposition | - | +| Extra Trees Algorithm | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_2/DTE-LECTURE-2-TREE-VARIANCE.ipynb)| +| Bias-variance and Randomized Ensembles | - | + + +Lecture 3 + +| title | studio lab | +| :---: | ---: | +| Boostrapping | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_3/DTE-LECTURE-3-BOOTSTRAP.ipynb)| +| Bagging |Bagging [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_3/DTE-LECTURE-3-BAGGING-OVERFIT.ipynb)
tree correlation [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_3/DTE-LECTURE-3-TREE-CORRELATION.ipynb)| +| Random Forests | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_3/DTE-LECTURE-3-RANDOM-FOREST.ipynb)| + + +Lecture 4 + +| title | studio lab | +| :---: | ---: | +| Random Forest Proximities | - | +| Some use cases for Proximities | - | +| Feature Importance in Trees | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_4/DTE-LECTURE-4-PERMUTATION-FEATURE-IMP.ipynb)| +| Feature Importance in Random Forests |[![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_4/DTE-LECTURE-4-FEATURE-IMPORTANCE.ipynb) | + + +Lecture 5 + +| title | studio lab | +| :---: | ---: | +| Boosting | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_5/DTE-LECTURE-5-BOOSTING.ipynb)| +| Gradient Boosting | - | +| XGBoost, LightGBM and CatBoost | CatBoost [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_5/DTE-LECTURE-5-CATBOOST.ipynb)
LightGBM [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_5/DTE-LECTURE-5-LIGHTGBM.ipynb)| + +Final Project + +| title | studio lab | +| :---: | ---: | +| Final Project | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/final_project/DTE-FINAL-PROJECT.ipynb)| + __Final Project:__ Practice working with a "real-world" computer vision dataset for the final project. Final project dataset is in the [data/final_project folder](https://github.com/aws-samples/aws-machine-learning-university-dte/tree/main/data/final_project). For more details on the final project, check out [this notebook](https://github.com/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/final_project/DTE-FINAL-PROJECT.ipynb).