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Beginning Mathematica and Wolfram for data science = applications in data analysis, machine learning, and neural networks /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Beginning Mathematica and Wolfram for data science/ by Jalil Villalobos Alva.
Reminder of title:	applications in data analysis, machine learning, and neural networks /
Author:	Villalobos Alva, Jalil.
Published:	Berkeley, CA :Apress : : 2024.,
Description:	xxiii, 462 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Machine Learning. -
Online resource:	https://doi.org/10.1007/979-8-8688-0348-2
ISBN:	9798868803482

Beginning Mathematica and Wolfram for data science = applications in data analysis, machine learning, and neural networks /
Villalobos Alva, Jalil.

Beginning Mathematica and Wolfram for data scienceapplications in data analysis, machine learning, and neural networks /[electronic resource] :by Jalil Villalobos Alva. - Second edition. - Berkeley, CA :Apress :2024. - xxiii, 462 p. :ill., digital ;24 cm.

1. Introduction to Mathematica -- 2. Data Manipulation -- 3. Working with Data and Datasets -- 4. Import and Export -- 5. Data Visualization -- 6. Statistical Data Analysis -- 7. Data Exploration -- 8. Machine Learning with the Wolfram Language -- 9. Neural Networks with the Wolfram Language -- 10. Neural Network Framework.

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. This second edition introduces the latest LLM Wolfram capabilities, delves into the exploration of data types in Mathematica, covers key programming concepts, and includes code performance and debugging techniques for code optimization. You'll gain a deeper understanding of data science from a theoretical and practical perspective using Mathematica and the Wolfram Language. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. Existing topics have been reorganized for better context and to accommodate the introduction of Notebook styles. The book also incorporates new functionalities in code versions 13 and 14 for imported and exported data. You'll see how to use Mathematica, where data management and mathematical computations are needed. Along the way, you'll appreciate how Mathematica provides an entirely integrated platform: its symbolic and numerical calculation result in a mized syntax, allowing it to carry out various processes without superfluous lines of code. You'll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out.

ISBN: 9798868803482

Standard No.: 10.1007/979-8-8688-0348-2doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: QA76.73.M29

Dewey Class. No.: 510.285536

Beginning Mathematica and Wolfram for data science = applications in data analysis, machine learning, and neural networks /
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