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Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.

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πŸ“š 50 Algorithms Every Programmer Should Know (2023)

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πŸ“š Graph Algorithms for Data Science (2023 - V9)

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πŸ“š Algorithms with JULIA (2024)

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πŸ“š Metaheuristic Algorithms (2024)

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πŸ“š Algorithms for Decision Making (2022)

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πŸ”— Machine Learning from Scratch by Danny Friedman

This book is for readers looking to learn new #machinelearning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different #algorithms create the models they do and the advantages and disadvantages of each one.

This book will be most helpful for those with practice in basic modeling. It does not review best practicesβ€”such as feature engineering or balancing response variablesβ€”or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.


https://dafriedman97.github.io/mlbook/content/introduction.html

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πŸ”₯ Trending Repository: Java

πŸ“ Description: All Algorithms implemented in Java

πŸ”— Repository URL: https://github.com/TheAlgorithms/Java

πŸ“– Readme: https://github.com/TheAlgorithms/Java#readme

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πŸ’» Programming Languages: Java - Dockerfile

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πŸ”₯ Trending Repository: Python

πŸ“ Description: All Algorithms implemented in Python

πŸ”— Repository URL: https://github.com/TheAlgorithms/Python

🌐 Website: https://thealgorithms.github.io/Python/

πŸ“– Readme: https://github.com/TheAlgorithms/Python#readme

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πŸ“Œ The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2025-11-30 | ⏱️ Read time: 19 min read

The Greedy Boruta algorithm offers a significant performance enhancement for feature selection. As a modification of the standard Boruta method, it dramatically reduces computation time. This speed increase is achieved without sacrificing recall, ensuring high sensitivity in identifying all relevant features. It's a powerful optimization for data scientists seeking to accelerate their machine learning workflows while preserving model quality.

#FeatureSelection #MachineLearning #DataScience #Algorithms