Walking Through Kaggle Tutorials With Visualizations
Ayush Chaurasia walks us through Kaggle tutorials, making them more accessible and adding visualization that shed light on what's happening under the hood.
Created on June 12|Last edited on June 14
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Kaggle is an outstanding place to not only compete with your fellow machine learning practitioners but also learn both by doing, and by taking their tutorials and courses. Below you will find write-ups of some of their more popular tutorials that I have followed along with, adding additional context as well as visualizations to help us better understand what's going on under the hood.
Let me know in the comments below or on individual tutorials if you have any questions or would like to see others covered.
The tutorials are divided into sections where applicable:
Tutorials From Kaggle Intermediate Machine Learning Course
In this section you'll find tutorials from Kaggle's Intermediate Machine Learning Course with added context and visualizations.
Handling Missing Values In A Pandas Dataframe
In this tutorial, you will learn three approaches to dealing with missing values in a pandas dataframe.
Handling Categorical Features - With Examples
In this report, you will learn what a categorical variable is, along with three approaches for handling this type of data.
Using K-Fold Cross-Validation To Improve Your Machine Learning Models
In this article, we will learn how to use k-fold cross-validation for better measures of machine learning model performance, using W&B to track our results.
Gradient Boosting With XGBoost
In this report, you will learn how to build and optimize models with gradient boosting. This method dominates many Kaggle competitions and achieves state-of-the-art results on a variety of datasets
Kaggle Machine Learning Tutorials
In this section you'll find some of the stand-alone tutorials we found very interesting but where added context and visualization could assist readers. For a full list of all their tutorials, you can visit their learning center.
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