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15 Math Concepts Every Data Scientist Should Know

You're reading from   15 Math Concepts Every Data Scientist Should Know Understand and learn how to apply the math behind data science algorithms

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781837634187
Length 510 pages
Edition 1st Edition
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Author (1):
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David Hoyle David Hoyle
Author Profile Icon David Hoyle
David Hoyle
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Essential Concepts
2. Chapter 1: Recap of Mathematical Notation and Terminology FREE CHAPTER 3. Chapter 2: Random Variables and Probability Distributions 4. Chapter 3: Matrices and Linear Algebra 5. Chapter 4: Loss Functions and Optimization 6. Chapter 5: Probabilistic Modeling 7. Part 2: Intermediate Concepts
8. Chapter 6: Time Series and Forecasting 9. Chapter 7: Hypothesis Testing 10. Chapter 8: Model Complexity 11. Chapter 9: Function Decomposition 12. Chapter 10: Network Analysis 13. Part 3: Selected Advanced Concepts
14. Chapter 11: Dynamical Systems 15. Chapter 12: Kernel Methods 16. Chapter 13: Information Theory 17. Chapter 14: Non-Parametric Bayesian Methods 18. Chapter 15: Random Matrices 19. Index 20. Other Books You May Enjoy

Machine learning approaches to time series analysis

Everything we have explained so far in this chapter may give the impression that ARIMA modeling is the only approach to time series modeling available. This is certainly not true. The rapid development and success of machine learning techniques over the last two decades has inevitably meant that machine learning algorithms have been applied to time series datasets.

However, the field of machine learning algorithms applied to time series is probably comparable to or bigger than that of ARIMA modeling, and it is still a rapidly developing field. As with other branches of machine learning, it is a very applied field. There are not simple clear-cut mathematical concepts or principles that define the field of machine learning for time series analysis, like there are for ARIMA analysis of time series. Therefore, in this section, we focus on giving you a short review of how the field has developed and its current state. Since it is a...

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