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Overview: Machine Learning (ML) has rapidly grown into a pivotal technology, transforming various industries by enabling data-driven decision-making and predictive analytics. As the volume and complexity of data grows, the ability to leverage sophisticated ML algorithms becomes primary for researchers, practitioners, and enthusiasts alike. This book aims to serve as a rich resource, addressing most of the algorithms, methods, and techniques used in this field. Offering insights into each method’s theoretical foundations, practical applications, and strengths and weaknesses, also provides a robust framework for selecting the most appropriate technique for many projects and research endeavors. This book has provided a comprehensive overview of various Machine Learning algorithms and techniques, categorized by their primary functions such as regression, classification, clustering, optimization, and NLP. Each algorithm has been explained in terms of its main concept, purpose, use cases, mathematical background, loss function, pros and cons, and visual representation. This knowledge is crucial for selecting the right algorithm for specific problems and understanding the underlying mechanisms that drive their performance. This book is designed to help demystify the complex world of ML algorithms. By providing clear and cutting-edge explanations of each method, along with practical examples and illustrative figures, aiming to bridge the gap between practical implementation and theoretical knowledge. Whether you are working on stock price prediction, energy consumption forecasting, natural language processing (NLP), or image recognition, this guide will supply you with the knowledge needed to make informed decisions about which ML methods to utilize. The reader is invited to delve into the fascinating ML world in this book, uncover the methods, and pave the power of data to create proper solutions and drive progress in such an important domain.
Genre: Non-Fiction > Tech & Devices
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