You're reading from Principles of Data Science A beginner's guide to essential math and coding skills for data fluency and machine learning

Product type Paperback

Published in Jan 2024

Publisher Packt

ISBN-13 9781837636303

Length 326 pages

Edition 3rd Edition

Languages

Python

Tools

MLOps

Concepts

Data Science

Author (1):

Sinan Ozdemir

View More author details

Table of Contents (18) Chapters

Preface

1. Chapter 1: Data Science Terminology

2. Chapter 2: Types of Data FREE CHAPTER

3. Chapter 3: The Five Steps of Data Science

4. Chapter 4: Basic Mathematics

5. Chapter 5: Impossible or Improbable – A Gentle Introduction to Probability

6. Chapter 6: Advanced Probability

7. Chapter 7: What Are the Chances? An Introduction to Statistics

8. Chapter 8: Advanced Statistics

9. Chapter 9: Communicating Data

10. Chapter 10: How to Tell if Your Toaster is Learning – Machine Learning Essentials

11. Chapter 11: Predictions Don’t Grow on Trees, or Do They?

12. Chapter 12: Introduction to Transfer Learning and Pre-Trained Models

13. Chapter 13: Mitigating Algorithmic Bias and Tackling Model and Data Drift

14. Chapter 14: AI Governance

15. Chapter 15: Navigating Real-World Data Science Case Studies in Action

16. Index

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Summary

As we reach the conclusion of this comprehensive case study chapter, it’s important to highlight that the journey doesn’t end here. The power of modern ML and AI is vast and ever-growing, and there is always more to learn, explore, and create.

Our official GitHub repository serves as a central hub, housing not only the code and detailed explanations from this case study but also an extensive collection of additional resources, examples, and even more intricate case studies:

More case studies: Dive deeper into the world of ML with an array of case studies spanning various domains and complexities. Each case study is meticulously crafted to provide you with hands-on experience, guiding you through different challenges and solutions in the AI landscape.
Comprehensive code examples: The repository is rich with code examples that complement the case studies and explanations provided. These examples are designed to be easily understandable and executable...