π 50 Algorithms Every Programmer Should Know (2023)
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π Graph Algorithms for Data Science (2023 - V9)
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π Algorithms with JULIA (2024)
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π Metaheuristic Algorithms (2024)
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π Algorithms for Decision Making (2022)
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Forwarded from Machine Learning with Python
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 practicesβsuch as feature engineering or balancing response variablesβor 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
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
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π Description: All Algorithms implemented in Java
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π₯ Trending Repository: Python
π Description: All Algorithms implemented in Python
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π The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall
π Category: MACHINE LEARNING
π Date: 2025-11-30 | β±οΈ 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
π Category: MACHINE LEARNING
π Date: 2025-11-30 | β±οΈ 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