Kaggle: Machine Learning Series

This repository contains a series of Jupyter notebooks (.ipynb) that document my journey through Kaggle's Machine Learning and Deep Learning exercises.

Environment Setup

These notebooks are intended to be run on Kaggle. If you wish to run them locally, make sure you have activated the required environment using Conda:

conda activate sklearn-env

Adding a New Package

If you need to add a new package, use the following commands:

# Using conda-forge
conda install -c conda-forge <package>

Comment:
This repository contains a collection of introductory notebooks and forked tutorials from other Kagglers exploring Machine Learning, with a focus on Deep Learning. I'm using Kaggle to learn and practice my skills in these fields. The repository is primarily maintained through Kaggle, not GitHub, so there may be discrepancies in content. To view the original code, visit my Kaggle profile here.

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Please consider giving it a star. Your support helps me continue to develop high-quality code and pursue my career as a data analyst/engineer. Feedback is always welcome and appreciated. Thank you for taking the time to read my work!

Name		Name	Last commit message	Last commit date
Latest commit History 246 Commits
archive		archive
.gitignore		.gitignore
README.md		README.md
arxiv-opencontrail-nutshell.ipynb		arxiv-opencontrail-nutshell.ipynb
context-for-goes16-contrails .ipynb		context-for-goes16-contrails .ipynb
contrail-formation-climate-impact.ipynb		contrail-formation-climate-impact.ipynb
contrail-net-vision.ipynb		contrail-net-vision.ipynb
contrails-init-data-stats-histograms.ipynb		contrails-init-data-stats-histograms.ipynb
contrails_illustrated_booklet.ipynb		contrails_illustrated_booklet.ipynb
directory-tree-generator.ipynb		directory-tree-generator.ipynb
directory_tree_generator.ipynb		directory_tree_generator.ipynb
fork-of-getting-started-eda-model-train.ipynb		fork-of-getting-started-eda-model-train.ipynb
global-food-security.ipynb		global-food-security.ipynb
goes16-contrail-detection-rle-encoding.ipynb		goes16-contrail-detection-rle-encoding.ipynb
insights-dnn-diagnostics-activations-gradients.ipynb		insights-dnn-diagnostics-activations-gradients.ipynb
roboarm-finding-shortest-path.ipynb		roboarm-finding-shortest-path.ipynb
tests-a-simple-soil-data-pipeline.ipynb		tests-a-simple-soil-data-pipeline.ipynb
umbelliferae-seed-classifier.ipynb		umbelliferae-seed-classifier.ipynb
uses-of-shap-values.ipynb		uses-of-shap-values.ipynb
utils-contrails-loss-functions.ipynb		utils-contrails-loss-functions.ipynb
visualize-contrail-false-color-human-masks.ipynb		visualize-contrail-false-color-human-masks.ipynb

patmejia/kaggle_ml

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Kaggle: Machine Learning Series

Environment Setup

Adding a New Package

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