You're reading from Synthetic Data for Machine Learning Revolutionize your approach to machine learning with this comprehensive conceptual guide

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781803245409

Length 208 pages

Edition 1st Edition

Concepts

Data Science

Author (1):

Abdulrahman Kerim

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Table of Contents (25) Chapters

Preface

1. Part 1:Real Data Issues, Limitations, and Challenges

2. Chapter 1: Machine Learning and the Need for Data FREE CHAPTER

3. Chapter 2: Annotating Real Data

4. Chapter 3: Privacy Issues in Real Data

5. Part 2:An Overview of Synthetic Data for Machine Learning

6. Chapter 4: An Introduction to Synthetic Data

7. Chapter 5: Synthetic Data as a Solution

8. Part 3:Synthetic Data Generation Approaches

9. Chapter 6: Leveraging Simulators and Rendering Engines to Generate Synthetic Data

10. Chapter 7: Exploring Generative Adversarial Networks

11. Chapter 8: Video Games as a Source of Synthetic Data

12. Chapter 9: Exploring Diffusion Models for Synthetic Data

13. Part 4:Case Studies and Best Practices

14. Chapter 10: Case Study 1 – Computer Vision

15. Chapter 11: Case Study 2 – Natural Language Processing

16. Chapter 12: Case Study 3 – Predictive Analytics

17. Chapter 13: Best Practices for Applying Synthetic Data

18. Part 5:Current Challenges and Future Perspectives

19. Chapter 14: Synthetic-to-Real Domain Adaptation

20. Chapter 15: Diversity Issues in Synthetic Data

21. Chapter 16: Photorealism in Computer Vision

22. Chapter 17: Conclusion

23. Index

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24. Other Books You May Enjoy

What is predictive analytics?

In this section, you will be introduced to predictive analytics. You will understand its various applications in practice. You will also explore its wide application in different fields, including banking, finance, and healthcare.

Predicting the future has always been a central concern and has captivated humankind for thousands of years, and it’s still a hot topic! Predicting the future helps to identify and exploit opportunities, optimize resource allocation, and prepare better for catastrophes, disasters, and crises. Predictive analytics is a subfield of data analysis that aims to utilize historical data to predict or forecast the future. Now, to embark on our journey of understanding predictive analytics, let’s examine some of its interesting applications.

Applications of predictive analytics

There are enormous applications of predictive analytics in practice. A sample of these applications is shown in Figure 12.1.

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You're reading from Synthetic Data for Machine Learning Revolutionize your approach to machine learning with this comprehensive conceptual guide

Table of Contents (25) Chapters

What is predictive analytics?

Applications of predictive analytics

Authors (1)

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You're reading from Synthetic Data for Machine Learning Revolutionize your approach to machine learning with this comprehensive conceptual guide

Table of Contents (25) Chapters

What is predictive analytics?

Applications of predictive analytics

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