RadwaSK/MLGuideNotebooks

Collection of notebooks I made to illustrate some machine learning concepts and models in this repo, most of the models in this repo are built once from scratch and once using built-in models from libraries like sklearn.

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This collection of notebooks provides clear, step-by-step examples of how various machine learning models work, from simple linear regression to more advanced techniques like XGBoost. You'll see how to prepare data, build a model from scratch, and then apply it using standard libraries to predict outcomes or classify data. This is for anyone learning about machine learning concepts, whether you're a student, an analyst looking to expand your skills, or an aspiring data scientist.

No commits in the last 6 months.

Use this if you want to understand the inner workings of common machine learning algorithms and see them applied to real-world datasets like airline ticket prices or the Titanic passenger list.

Not ideal if you're looking for an advanced, production-ready machine learning framework or pre-built solutions for complex enterprise problems.

machine-learning-education data-science-training predictive-modeling statistical-analysis algorithmic-understanding
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 4 / 25
Maturity 16 / 25
Community 13 / 25

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Language

Jupyter Notebook

License

MIT

Last pushed

Mar 26, 2022

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