Fundamentals of Machine Learning: a Simplified ApproachКНИГИ » ПРОГРАММИНГ
Название: Fundamentals of Machine Learning: a Simplified Approach Автор: Er. Sudhir Goswami, Nirvikar Katiyar, Pradeep Kumar, K.P. Sharma, Mamta Tiwari Издательство: Blue Duck Publications Год: 2024 Страниц: 312 Язык: английский Формат: pdf, epub (true), mobi Размер: 10.1 MB
This book aims to serve as a comprehensive introduction to the principles, algorithms, and applications of Machine Learning, with a particular emphasis on both foundational and cutting-edge techniques. It is designed for students, researchers, and practitioners who wish to deepen their understanding of Machine Learning concepts, explore their practical applications, and grasp the underlying mathematical frameworks.
The content spans a broad range of topics, beginning with fundamental Machine Learning concepts, such as regression, decision trees, and artificial neural networks. It then delves into specialized domains like support vector machines, Reinforcement Learning, and convolutional neural networks. Along the way, readers will encounter detailed explanations of key algorithms, including ID3, Q-learning, and deep Q-learning, as well as practical insights into their implementation. Real-world case studies—such as diabetic retinopathy diagnosis using convolutional neural networks, building smart speakers, and self-driving cars—illustrate the transformative potential of these techniques.
While the book is technical in nature, it is also accessible to those new to the field. Complex mathematical equations have been minimized or simplified where possible to focus on the intuition and mechanics behind the algorithms. This approach ensures that readers can grasp the concepts without being overwhelmed by formalism, while still providing the depth required for advanced learners.
This book is not just a guide to mastering Machine Learning algorithms; it is an invitation to explore the limitless possibilities of intelligent systems. As you navigate these pages, you will discover how learning models evolve, how machines perceive and respond to the world, and how this knowledge can be harnessed to solve some of humanity’s most pressing problems.
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