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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you...

Mastering Numerical Computing with NumPy: Master scientific computing and perform complex operations with ease
Mastering Numerical Computing with NumPy: Master scientific computing and perform complex operations with ease

Enhance the power of NumPy and start boosting your scientific computing capabilities

Key Features

  • Grasp all aspects of numerical computing and understand NumPy
  • Explore examples to learn exploratory data analysis (EDA), regression, and clustering
  • Access...
C# 7 Quick Syntax Reference: A Pocket Guide to the Language, APIs, and Library
C# 7 Quick Syntax Reference: A Pocket Guide to the Language, APIs, and Library
This quick C# 7 guide is a condensed code and syntax reference to the C# programming language, updated with the latest features of C# 7.3 for .NET and Windows 10. It presents the essential C# 7 syntax in a well-organized format that can be used as a handy reference.


In the C# 7 Quick Syntax...
Practical PowerPivot & DAX Formulas for Excel 2010
Practical PowerPivot & DAX Formulas for Excel 2010

PowerPivot: What a name! Or I should really say what’s in a name? Powerful Pivoting? Pivot-table for power users?

PowerPivot is a lot more than that. When I started the PowerPivot project back in 2007, along with Amir Netz (Microsoft BI Distinguished Engineer), the incubation project was called BI Sandbox. We had been looking...

Machine Learning with R - Second Edition
Machine Learning with R - Second Edition

Key Features

  • Harness the power of R for statistical computing and data science
  • Explore, forecast, and classify data with R
  • Use R to apply common machine learning algorithms to real-world scenarios

Book Description

Machine learning, at its core, is concerned...

Unknown Quantity: A Real and Imaginary History of Algebra
Unknown Quantity: A Real and Imaginary History of Algebra
Prime Obsession taught us not to be afraid to put the math in a math book. Unknown Quantity heeds the lesson well. So grab your graphing calculators, slip out the slide rules, and buckle up! John Derbyshire is introducing us to algebra through the ages—and it promises to be just what his die-hard fans have been waiting...
How to Pass Data Interpretation Tests: Unbeatable Practice for Numerical and Quantitative Reasoning and Problem Solving Tests (Testing Series)
How to Pass Data Interpretation Tests: Unbeatable Practice for Numerical and Quantitative Reasoning and Problem Solving Tests (Testing Series)
This book provides everything you need for a successful programme of revision or review. It contains 330 practice questions with answers and explanations, advice on how to show your true potential and signposts to sources of further practice material.

Tests of data interpretation are fast
...
Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging (The Wiley Finance Series)
Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging (The Wiley Finance Series)

Supercharge options analytics and hedging using the power ofPython

Derivatives Analytics with Python shows you how toimplement market-consistent valuation and hedging approaches usingadvanced financial models, efficient numerical techniques, and thepowerful capabilities of the Python programming language. Thisunique...

Assessing and Improving Prediction and Classification: Theory and Algorithms in C++
Assessing and Improving Prediction and Classification: Theory and Algorithms in C++
Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting.  This book presents many important techniques for building...
Learning R for Geospatial Analysis
Learning R for Geospatial Analysis

Leverage the power of R to elegantly manage crucial geospatial analysis tasks

About This Book

  • Write powerful R scripts to manipulate your spatial data
  • Gain insight from spatial patterns utilizing R's advanced computation and visualization capabilities
  • Work within a...
Introduction to Computer Science
Introduction to Computer Science
A computer program is a set of statements that is used to create an output, such as a screen display, a printed report, a set of data records, or a calculated set of numbers.

Most programs involve statements that are executed in sequence.

A program is written using the statements of a programming language.
...
Knowledge-Based Neurocomputing
Knowledge-Based Neurocomputing
Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based...
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