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The aim is to provide a thorough understanding of machine learning theories, applications, and challenges.

April 10, 2023ElectronicsOpen Access

Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms

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The aim is to provide a thorough understanding of machine learning theories, applications, and challenges.
Reviewed various machine learning algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning.
Explored applications across fields such as healthcare, finance, and autonomous driving.
Discussed challenges in model building and gaps in current research.
Provided insights into the effectiveness of different machine learning methods across various industries.
Identified key challenges and gaps that hinder the development of advanced machine learning models.
Outlined the role of data protection laws in shaping machine learning applications globally.

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Comprehensive review demonstrates machine learning applications and challenges across various sectors, highlighting its importance.

Tufail et al. (2023) studied this question.

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