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Principles of data science

Tác giả: Hamid Arabnia, ... [and other people]

This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data ...

Codeless data structures and algorithms : learn DSA without writing a single line of code

Tác giả: Armstrong Subero

In the era of self-taught developers and programmers, essential topics in the industry are frequently learned without a formal academic foundation. A solid grasp of data structures and algorithms (DSA) is imperative for anyone looking to do professional software development and engineering, but ...

Handbook of big data analytics : 1st ed.

Tác giả: Wolfgang Karl Härdle, Henry Horng-Shing Lu, Xiaotong Shen

Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation ...

Mastering machine learning with python in six steps : a practical implementation guide to predictive data analytics using python : 2nd ed.

Tác giả: Manohar Swamynathan

You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient ...

Hands-on gradient boosting with XGBoost and scikit-learn : perform accessible machine learning and extreme gradient boosting with python

Tác giả: Corey Wade

The book introduces machine learning and XGBoost in scikit-learn before building up to the theory behind gradient boosting. You'll cover decision trees and analyze bagging in the machine learning context, learning hyperparameters that extend to XGBoost along the way. You'll build gradient boosting ...