Machine Learning Principles Algorithms And Tools
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Machine Learning: Principles, Algorithms, and Tools
Author: Dr Saroj Kumar Nanda
language: en
Publisher: Addition Publishing House
Release Date: 2024-12-02
Machine learning is reshaping the world, powering advancements in artificial intelligence, automation, and data-driven decision-making. As industries increasingly rely on intelligent systems, understanding how machines learn from data has become more essential than ever. This book is designed to introduce readers to the fundamental principles of machine learning in a structured and accessible manner. It breaks down complex concepts into easy-to-understand explanations, guiding readers through the process of building intelligent systems. Whether you are a student, a professional, or simply curious about AI, this book provides a solid foundation to grasp the core ideas behind machine learning. The significance of machine learning extends beyond just technology; it influences healthcare, finance, marketing, and various other fields. By understanding its principles, individuals and organizations can unlock new opportunities, optimize processes, and make smarter predictions. This book aims to bridge the gap between theoretical understanding and practical implementation, encouraging readers to think critically and explore real-world applications. As you navigate through the chapters, you will discover how machines analyze patterns, adapt to data, and improve over time. The journey into machine learning is both exciting and challenging, but with the right approach, it can be highly rewarding. This book serves as a stepping stone for anyone eager to explore the potential of intelligent systems and how they shape the future.
Fundamentals of Data Science DataMining MachineLearning DeepLearning and IoTs
Author: Dr. P. Kavitha
language: en
Publisher: Leilani Katie Publication
Release Date: 2023-12-23
Dr. P. Kavitha, Associate Professor, Department of Computer Science, Sri Ramakrishna College of Arts & Science, Coimbatore, Tamil Nadu, India. Mr. P. Jayasheelan, Assistant Professor, Department of Computer Science, Sri Krishna Aditya College of arts and Science, Coimbatore, Tamil Nadu, India. Ms. C. Karpagam, Assistant Professor, Department of Computer Science with Data Analytics, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India. Dr. K. Prabavathy, Assistant Professor, Department of Data Science and Analytics, Sree Saraswathi Thyagaraja College, Pollachi, Coimbatore, Tamil Nadu, India.
Machine Learning for Dynamic Software Analysis: Potentials and Limits
Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts. This volume originates from a Dagstuhl Seminar entitled "Machine Learning for Dynamic Software Analysis: Potentials and Limits” held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities. The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches.