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Machine Learning University: Accelerated Tabular Data Class
| Date | Stars |
|---|---|
| 2026-07-31 | 1028 |
| 2026-08-02 | 1029 |
| 2026-08-06 | 1029 |
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 ## Machine Learning University: Accelerated Tabular Data Class This repository contains __slides__, __notebooks__, and __datasets__ for the __Machine Learning University (MLU) Accelerated Tabular Data__ class. Our mission is to make Machine Learning accessible to everyone. We have courses available across many topics of machine learning and believe knowledge of ML can be a key enabler for success. This class is designed to help you get started with tabular data (spreadsheet-like tables), learn about widely used Machine Learning techniques for tabular data, and apply them to real-world problems. ## YouTube Watch all Tabular Data class video recordings in this [YouTube playlist](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuVQZCYf_ZnMoIWLLKGx9Mi2) from our [YouTube channel](https://www.youtube.com/channel/UC12LqyqTQYbXatYS9AA7Nuw/playlists). [](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuVQZCYf_ZnMoIWLLKGx9Mi2) ## Course Overview There are three lectures and one final project for this class. Lecture 1 | title | studio lab | | :---: | ---: | | Introduction to ML | - | | Sample ML Model | - | | Model Evaluation | [](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/main/notebooks/MLA-TAB-DAY1-MODEL.ipynb) | | Exploratory Data Analysis | [](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/main/notebooks/MLA-TAB-DAY1-EDA.ipynb) | | K Nearest Neighbors (KNN) | [](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/main/notebooks/MLA-TAB-DAY1-KNN.ipynb) | | Final Project | [](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/main/notebooks/MLA-TAB-DAY1-FINAL.ipynb) | Lecture 2 | title | studio lab | | :---: | ---: | |Feature Engineering | [](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/main/notebooks/MLA-TAB-DAY2-TEXT-PROCESS.ipynb) | | Tree-based Models | [](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/main/notebooks/MLA-TAB-DAY2-TREE.ipynb) | | Bagging | - | | Hyperparameter Tuning | - | | AWS AI/ML Services |[](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/main/notebooks/MLA-TAB-DAY2-SAGEMAKER.ipynb) | Lecture 3 | title | studio lab | | :---: | ---: | | Optimization | - | | Regression Models | - | | Boosting | - | | Neural Networks |NN [](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/main/notebooks/MLA-TAB-DAY3-NN.ipynb) | | AutoML |[](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/main/notebooks/MLA-TAB-DAY3-AUTOML.ipynb) | **Final Project:** Practice working with a "real-world" tabular dataset for the final project. Final project dataset is in the [data/final_project folder](https://github.com/aws-samples/aws-machine-learning-university-accelerated-tab/tree/main/data/final_project). For more details on the final project, check out [this notebook](https://github.com/aw
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:00f2cd4fb8f6cdc0, topic:deep-learning