Creating course content to reflect my own journey in Machine Learning.
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Structured as tutorials or exercises or projects - Choose your style
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Available on Colab or Kaggle or Binder or GitHub - Choose your platform
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Suitable for beginners or intermediates or experts - Choose your expertise
- DatatableTon: π― datatable exercises: Exercises & tutorials to teach & learn Python datatable
The AutoML series explores various open source AutoML libraries on Kaggle's Tabular Playground Series (TPS) competitions:
TPS Notebook | Type | AutoGluon | EvalML | FLAML | H2OAutoML | LightAutoML | MLJAR | TPOT |
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Sep 2021 | Binary | β | β | β | β | β | β | β |
Aug 2021 | Regression | β | β | β | β | β | ||
Jul 2021 | Regression | β | β | β | β | β | β | |
Jun 2021 | Multiclass | β | ||||||
May 2021 | Multiclass | β | ||||||
Apr 2021 | Binary | β | β | β | β | β | β | β |
Mar 2021 | Binary | β | β | β | β | β | β | β |
Feb 2021 | Regression | β | ||||||
Jan 2021 | Regression | β |
- Introducing DatatableTon: Python Datatable Tutorials & Exercises
- Progressively approaching Kaggle: Getting started with Kaggle competitions using the Tabular Playground Series
- Grandmaster Quotes: The ultimate Kaggle Cheat Sheet by top Grandmasters
Please create a Discussion to request for content on a particular topic.
Please create an Issue for any improvements, suggestions or errors in the content.
You can also tag @vopani on Twitter for any other queries or feedback.
This project is licensed under the Apache License 2.0.