Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics (Little Max A.)(Pevná vazba)

Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics (Little Max A.)(Pevná vazba)

This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Taking a gradual approach, it builds up concepts in a

Data Mining and Machine Learning: Fundamental Concepts and Algorithms (Zaki Mohammed J.)(Pevná vazba)

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and

Fundamentals of Machine Learning for Predictive Data Analytics, Second Edition: Algorithms, Worked Examples, and Case Studies (Kelleher John D.)(Pevná vazba)

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data

Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing (Beysolow II Taweh)(Paperback)

Chapter 1: What is Natural Language Processing? Chapter Goal: Establishing understanding of topic and give overview of textNo of pages: 10 pagesSub -Topics1. History of Natural Language Processing 2. Word Embeddings3. Neural Networks applied to Natural Language Processing 4. Python Packages Chapter

Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes (Panesar Arjun)(Paperback)

This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data.The book has been updated with the latest research in massive data,

Natural Language Processing: A Machine Learning Perspective (Zhang Yue)(Pevná vazba)

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature,

Simulation Algorithms for Computational Systems Biology (Marchetti Luca)(Pevná vazba)

Essential topic in modern life science researchSelf-contained presentationAuthors are leading researchers in the

Statistics for Health Data Science: An Organic Approach (Etzioni Ruth)(Pevná vazba)

Machine Learning Refined: Foundations, Algorithms, and Applications (Watt Jeremy)(Pevná vazba)

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the

Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python (Tatsat Hariom)(Paperback)

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies

Advanced Statistics for the Behavioral Sciences: A Computational Approach with R (Brown Jonathon D.)(Pevná vazba)

Teaches core subjects students need to both understand and perform computer-generated statistical analyses2. Builds on Brown's earlier book on common statistical analyses, Linear Models in Matrix Form​ (Springer, 2014)3. Utilizes R as standard tool for algebraic calculations

Computational Methods for Deep Learning: Theoretic, Practice and Applications (Yan Wei Qi)(Pevná vazba)

In this book, we work for the contents for knowledge transfer from the viewpoint of machine intelligence. We adopt the methodology from graphical theory, mathematical models, algorithmic implementation as well as datasets preparation, programming, results analysis and evaluations. We start from

Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning (Gutman Alex J.)(Paperback)

'Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful.'Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You've heard the hype around data--now get the facts. In Becoming a Data

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (Brunton Steven L.)(Pevná vazba)

Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling and control of dynamical systems with modern methods in data science. It

Introduction to Natural Language Processing (Eisenstein Jacob)(Pevná vazba)

A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques.This textbook provides a technical perspective on natural language processing--methods

Biocalculus: Calculus, Probability, and Statistics for the Life Sciences (Stewart James)(Pevná vazba)

BIOCALCULUS: CALCULUS, PROBABILITY, AND STATISTICS FOR THE LIFE SCIENCES shows students how calculus relates to biology, with a style that maintains rigor without being overly formal. The text motivates and illustrates the topics of calculus with examples drawn from many areas of biology, including

Computational Approaches to the Network Science of Teams (Li Liangyue)(Pevná vazba)

This comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in team science. Focusing on underlying network structures, it presents models and algorithms for characterizing, predicting, optimizing, and explaining team performance, plus key

Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions (Taddy Matt)(Pevná vazba)

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.Use machine learning to understand your customers, frame decisions, and drive value The business analytics

Computational Signal Processing and Analysis: Select Proceedings of Icnets2, Volume I (Nandi Asoke K.)(Pevná vazba)

Covers key developments in the field of electronics and communication engineeringIncludes latest research from field of Computational Signal Processing and AnalysisIncludes contributions from top researchers and

Bandit Algorithms (Lattimore Tor)(Pevná vazba)

Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including

Bayesian Reasoning and Machine Learning (Barber David)(Pevná vazba)

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who

Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks (Villalobos Alva Jalil)(Paperback)

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book will introduce you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and

Statistical Learning with Math and R: 100 Exercises for Building Logic (Suzuki Joe)(Paperback)

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building R programs. As the preliminary

Elements of Causal Inference: Foundations and Learning Algorithms (Peters Jonas)(Pevná vazba)

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a