Introduction to Data Science and Machine Learning
eBook - PDF

Introduction to Data Science and Machine Learning

  1. 232 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Introduction to Data Science and Machine Learning

About this book

and ldquo;Introduction to Data Science and Machine Learning and rdquo; has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish. This book is divided into four sections: the first section contains an introduction to the book, the second covers the field of data science, software development, and open-source based embedded hardware; the third section covers algorithms that are the decision engines for data science applications; and the final section brings together the concepts shared in the first three sections and provides several examples of data science applications.

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Yes, you can access Introduction to Data Science and Machine Learning by Keshav Sud,Pakize Erdogmus,Seifedine Kadry, Keshav Sud, Pakize Erdogmus, Seifedine Kadry in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Mining. We have over one million books available in our catalogue for you to explore.

Information

Publisher
IntechOpen
Year
2020
Print ISBN
9781838803339
eBook ISBN
9781838803711
Topic
Computer Science
Subtopic
Data Mining
Index
Computer Science

Table of contents

  1. Introduction to Data Science and Machine Learning
  2. Contents
  3. Preface
  4. Section 1 - Introduction
  5. Chapter 1 - Introductory Chapter: Clustering with Nature-Inspired Optimization Algorithms
  6. Section 2 - System Design and Architecture
  7. Chapter 2 - Best Practices in Accelerating the Data Science Process in Python
  8. Chapter 3 - Software Design for Success
  9. Chapter 4 - Embedded Systems Based on Open Source Platforms
  10. Section 3 - Algorithms
  11. Chapter 5 - The K-Means Algorithm Evolution
  12. Chapter 6 - “Set of Strings” Framework for Big Data Modeling
  13. Chapter 7 - Investigation of Fuzzy Inductive Modeling Method in Forecasting Problems
  14. Section 4 - Applications
  15. Chapter 8 - Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms
  16. Chapter 9 - The Software to the Soft Target Assessment
  17. Chapter 10 - The Methodological Standard to the Assessment of the Traffic Simulation in Real Time
  18. Chapter 11 - Augmented Post Systems: Syntax, Semantics, and Applications
  19. Chapter 12 - Serialization in Object-Oriented Programming Languages