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This is an introductory guide to machine learning (ML), covering fundamental concepts and various types including supervised, unsupervised, self-supervised, reinforcement, and semi-supervised learning.
The guide details the applications of each type, highlighting key differences between supervised and unsupervised approaches. It also addresses common challenges in ML such as data bias and the need for responsible AI development.
Finally, it explores the future of the field, focusing on emerging trends like generative AI and edge computing, and provides learning resources for aspiring practitioners.

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