Machine Learning: Fundamentals, Techniques, Applications and Challenges
Keywords:
Machine Learning, Artificial Intelligence, Deep Learning, Reinforcement Learning, Neural Networks, Transfer Learning, GANs, Applications of MLAbstract
Machine Learning (ML) has emerged as one of the most transformative technologies in the domain of Artificial Intelligence (AI). It draws its inspiration from the human ability to learn from experience and has become instrumental across healthcare, finance, environment, e-commerce and many other domains. Unlike traditional computational approaches, ML algorithms learn from data and improve their performance without being explicitly programmed. This paper presents a comprehensive analysis of ML by discussing its fundamentals, different learning paradigms (supervised, unsupervised, reinforcement), advanced techniques (deep learning, ensemble methods, transfer learning, GANs, transformers) and real-world applications. Furthermore, it highlights the ethical challenges such as bias, data privacy and transparency of algorithms that are emerging alongside technological growth. The aim of this study is to facilitate deeper understanding of ML concepts, explore its applicability and reflect on the societal implications of this rapidly growing field.
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