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Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner
Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner

Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence, Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing...

Kernel Smoothing: Principles, Methods and Applications
Kernel Smoothing: Principles, Methods and Applications

Comprehensive theoretical overview of kernel smoothing methods with motivating examples

Kernel smoothing is a flexible nonparametric curve estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods of kernel smoothing in a variety of...

The Cyber Risk Handbook: Creating and Measuring Effective Cybersecurity Capabilities (Wiley Finance)
The Cyber Risk Handbook: Creating and Measuring Effective Cybersecurity Capabilities (Wiley Finance)

Actionable guidance and expert perspective for real-world cybersecurity

The Cyber Risk Handbook is the practitioner's guide to implementing, measuring and improving the counter-cyber capabilities of the modern enterprise. The first resource of its kind, this book provides authoritative guidance for real-world...

Agile Model-Based Development Using UML-RSDS
Agile Model-Based Development Using UML-RSDS

This book describes the concepts and application of model-based development (MBD), model transformations, and Agile MBD to a wide range of software systems. It covers systems requirements engineering, system specification and design, verification, reuse, and system composition in the context of Agile MBD. Examples of applications in...

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource...

Introduction to Linear Optimization and Extensions with MATLAB® (Operations Research Series)
Introduction to Linear Optimization and Extensions with MATLAB® (Operations Research Series)

Filling the need for an introductory book on linear programming that discusses the important ways to mitigate parameter uncertainty, Introduction to Linear Optimization and Extensions with MATLAB® provides a concrete and intuitive yet rigorous introduction to modern linear optimization. In addition to fundamental topics,...

Simulation and Inference for Stochastic Differential Equations: With R Examples (Springer Series in Statistics)
Simulation and Inference for Stochastic Differential Equations: With R Examples (Springer Series in Statistics)

This book covers a highly relevant and timely topic that is of wide interest, especially in finance, engineering and computational biology. The introductory material on simulation and stochastic differential equation is very accessible and will prove popular with many readers. While there are several recent texts available that cover...

Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents
Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents

A practical guide to mastering reinforcement learning algorithms using Keras

Key Features

  • Build projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into action
  • Get to grips with Keras and practice on real-world unstructured...
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn

Key Features

  • Build a variety of Hidden Markov Models (HMM)
  • Create and apply models to any sequence of data to analyze, predict, and extract valuable...
Business Case Analysis with R: Simulation Tutorials to Support Complex Business Decisions
Business Case Analysis with R: Simulation Tutorials to Support Complex Business Decisions

This tutorial teaches you how to use the statistical programming language R to develop a business case simulation and analysis. It presents a methodology for conducting business case analysis that minimizes decision delay by focusing stakeholders on what matters most and suggests pathways for minimizing the risk in strategic and...

Data Professionals at Work
Data Professionals at Work
Enjoy reading interviews with more than two dozen data professionals to see a picture of what it’s like to work in the industry managing and analyzing data, helping you to know what it takes to move from your current expertise into one of the fastest growing areas of technology today. Data is the hottest word of...
Mastering Blockchain: Distributed ledger technology, decentralization, and smart contracts explained, 2nd Edition
Mastering Blockchain: Distributed ledger technology, decentralization, and smart contracts explained, 2nd Edition

Learn about cryptography and cryptocurrencies, so you can build highly secure, decentralized applications and conduct trusted in-app transactions.

Key Features

  • Get to grips with the underlying technical principles and implementations of blockchain
  • Build powerful...
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