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 Python | Machine Learning | Coding | R
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
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