Machine Learning Algorithms books

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Vadim Smolyakov , 2024
Anuradha Kar , 2024
Rod Stephens , 2024
Sergio Solórzano , 2023
Mark Ryan , 2021
Abdullah Karasan , 2022
Ariel Gamino , 2021
Stylianos Kampakis and Shreesha Jagadeesh , 2022
Nicole Königstein , 2022
Kanishka Tyagi & Raghavendra Sriram , 2022
Benjamin Soltoff , 2022
Shaked Zychlinski , 2022
Matheus Facure , 2022
Winnie Yeung and Eyan Yeung , 2021
Leonard Apeltsin, William Koehrsen, Nathan George, and Emre Rencberoglu , 2021
Alejandro Bellogin , 2021
Łukasz Kraiński and Bogumił Kamiński , 2021
Abdullah Karasan , 2021
Evan Hennis , 2021
Sean Owen, Robin Anil, Ted Dunning, and Ellen Friedman , 2011
Satnam Alag , 2008
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Dive into the world of machine learning algorithms, from foundational techniques to cutting-edge approaches. This comprehensive collection covers essential concepts in predictive modeling, deep learning architectures, and specialized applications across finance, sports analytics, computer vision, and natural language processing. Learn how to implement recommendation systems, detect anomalies, perform time series analysis, and leverage transformers for advanced AI applications. Whether you're interested in embedded systems, causal inference, or explainable AI, you'll find practical guidance on implementing these algorithms using popular tools and frameworks like Python, R, and Julia. Gain hands-on experience with real-world projects while building a solid theoretical foundation in the mathematical principles that power modern machine learning.