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Machine Learning: A Practical Approach on the Statistical Learning Theory

This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and;

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Introduction to Statistical Machine Learning

learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the;

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Machine Learning

This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and;

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An Elementary Introduction to Statistical Learning Theory

is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory;

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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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Hands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular;

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Practical Machine Learning

be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full;

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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine;

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Building Machine Learning Systems With Python

This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on;

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Neural Networks and Statistical Learning

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single;

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Statistical and Machine Learning Approaches for Network Analysis

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches;

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Mastering Machine Learning with R

, zero-cost statistical programming environment-there has never been a better time to start applying machine learning to your data. The book;

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Rank-Based Methods for Shrinkage and Selection

Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based;

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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to;

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Artificial Intelligence in Finance A PythonBased Guide

learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical;

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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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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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Evaluating Learning Algorithms

insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for;

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Evaluating Learning Algorithms

insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for;

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Machine Learning for Speaker Recognition

directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers;

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Machine Learning in Finance

covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective;

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Machine Learning in Finance

covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective;

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.;

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Python Machine Learning -

. About This Book * Second edition of the bestselling book on Machine Learning * A practical approach to key frameworks in data science, machine;

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Statistical Reinforcement Learning

field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms;

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Statistical Reinforcement Learning

field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms;

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