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Machine Learning Theoretical Foundations and Practical Applications

in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network;

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Machine Learning and Big Data

chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications;

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Theory and Practice of Computation

The papers included in the proceedings focus on both theoretical and practical aspects of computations, such as programming language theory;

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Machine Learning

provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical;

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Machine Learning

provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical;

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Practical Machine Learning with Rust

"Explore machine learning in Rust and learn about the intricacies of creating machine learning applications"--;

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Machine Learning and Deep Learning in Real-Time Applications

exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve;

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Practical Machine Learning with Rust

Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering;

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Mining the Web

. From Chakrabarti's work-painstaking, critical, and forward-looking-readers will gain the theoretical and practical understanding they need to;

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Modeling Machine Learning and Astronomy

This book constitutes the proceedings of the First International Conference on Modeling, Machine Learning and Astronomy, MMLA 2019, held;

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Understanding Machine Learning

introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the;

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Machine Learning in Industry

This book covers different machine learning techniques such as artificial neural network, support vector machine, rough set theory and deep;

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Conformal Prediction for Reliable Machine Learning

financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory;

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Foundations Of Machine Learning

Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms;

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Advances in Databases and Information Systems

from theoretical foundations to optimizing index structures. They focus on data mining and machine learning, data warehouses and big data;

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Machine Learning Applications Using Python

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies;

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Introducing Machine Learning

Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the;

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Machine Learning for iOS Developers

: Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics Build, deploy, and maintain ML;

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Immunoinformatics of Cancers

Immunoinformatics of Cancers: Practical Machine Learning Approaches Using R takes a bioinformatics approach to understanding and;

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Machine Learning With Go

readers the practical skills to perform the most common machine learning tasks with Go. Familiarity with some statistics and math topics;

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Introduction to Machine Learning with Python

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to;

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TensorFlow for Deep Learning From Linear Regression to Reinforcement Learning

you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how;

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Teaching Nursing

and clinical applications. The book examines the complexities of teaching and learning nursing, explains the theoretical foundations;

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A Primer on Machine Learning Applications in Civil Engineering

engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling;

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Applied Machine Learning for Smart Data Analysis

such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results. Key;

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Knowledge Acquisition and Machine Learning

last decade, which illustrate a particular way of unifying knowledge acquisition and machine learning. Practically-orientated, theoretical;

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Quantum Machine Learning

Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning;

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