Beginning Mathematica and Wolfram for Data Science : Applications in Data Analysis, Machine Learning, and Neural Networks / by Jalil Villalobos Alva.

Format:

Book

Author/Creator:

Alva, Jalil Villalobos, author.

Language:

English

Subjects (All):

Programming languages (Electronic computers).

Artificial intelligence--Data processing.

Artificial intelligence.

Machine learning.

Programming Language.

Data Science.

Machine Learning.

Local Subjects:

Programming Language.

Data Science.

Machine Learning.

Physical Description:

1 online resource (XXIII, 416 p. 344 illus., 54 illus. in color.)

Edition:

1st ed. 2021.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2021.

Summary:

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 disadvantages. You’ll see how to use the Wolfram language for data science from a theoretical and practical perspective. 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. You’ll cover how to use Mathematica where data management and mathematical computations are needed. Along the way you’ll appreciate how Mathematica provides a complete integrated platform: it has a mixed syntax as a result of its symbolic and numerical calculations 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. You will: Use Mathematica to explore data and describe the concepts using Wolfram language commands Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering.

Contents:

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.

Notes:

Includes index.

ISBN:

9781523150748

1523150742

9781484265949

1484265947