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Cracking the Data Science Interview

You're reading from   Cracking the Data Science Interview Unlock insider tips from industry experts to master the data science field

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781805120506
Length 404 pages
Edition 1st Edition
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Authors (2):
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Leondra R. Gonzalez Leondra R. Gonzalez
Author Profile Icon Leondra R. Gonzalez
Leondra R. Gonzalez
Aaren Stubberfield Aaren Stubberfield
Author Profile Icon Aaren Stubberfield
Aaren Stubberfield
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Breaking into the Data Science Field FREE CHAPTER
2. Chapter 1: Exploring Today’s Modern Data Science Landscape 3. Chapter 2: Finding a Job in Data Science 4. Part 2: Manipulating and Managing Data
5. Chapter 3: Programming with Python 6. Chapter 4: Visualizing Data and Data Storytelling 7. Chapter 5: Querying Databases with SQL 8. Chapter 6: Scripting with Shell and Bash Commands in Linux 9. Chapter 7: Using Git for Version Control 10. Part 3: Exploring Artificial Intelligence
11. Chapter 8: Mining Data with Probability and Statistics 12. Chapter 9: Understanding Feature Engineering and Preparing Data for Modeling 13. Chapter 10: Mastering Machine Learning Concepts 14. Chapter 11: Building Networks with Deep Learning 15. Chapter 12: Implementing Machine Learning Solutions with MLOps 16. Part 4: Getting the Job
17. Chapter 13: Mastering the Interview Rounds 18. Chapter 14: Negotiating Compensation 19. Index 20. Other Books You May Enjoy

Mastering Machine Learning Concepts

It’s time to give yourself a very generous pat on the back because you’ve officially arrived at the chapter on machine learning concepts. Take a moment to appreciate how far you’ve come, as well as all the preliminary information in the earlier chapters it takes to truly grasp this chapter. Many learners do themselves a disservice by jumping right into machine learning without first understanding its underlying principles (for example, statistics) and preliminary tasks (for example, data wrangling or pre-modeling), so this puts you ahead of the curve as someone well-equipped to understand the inner workings of machine learning algorithms and how and when to use them.

Throughout this chapter, we will cover a wide array of machine learning topics, providing you with the foundation needed to understand the intricacies of various algorithms and techniques. Our journey will begin with a detailed examination of the machine learning...

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