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Principles of Data Science

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 FREE CHAPTER

2. Chapter 2: Types of Data

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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17. Other Books You May Enjoy

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Authors (1)

Sinan Ozdemir

Sinan Ozdemir

Sinan is an active lecturer focusing on large language models and a former lecturer of data science at the Johns Hopkins University. He is the author of multiple textbooks on data science and machine learning including "Quick Start Guide to LLMs". Sinan is currently the founder of LoopGenius which uses AI to help people and businesses boost their sales and was previously the founder of the acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a Master's Degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco.

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