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#### Overview of this book

Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python. The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions. By the end of this book, you’ll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.
Preface
1. Fundamentals of Python
Free Chapter
2. Python's Main Tools for Statistics
3. Python's Statistical Toolbox
4. Functions and Algebra with Python
5. More Mathematics with Python
6. Matrices and Markov Chains with Python
7. Doing Basic Statistics with Python
8. Foundational Probability Concepts and Their Applications
9. Intermediate Statistics with Python
10. Foundational Calculus with Python
11. More Calculus with Python
12. Intermediate Calculus with Python

# Summary

In this chapter, you have been provided with a general and helpful exposition of the most central mathematical concepts in sequences, series, trigonometry, vectors, and complex numbers and, more importantly, their implementation in Python using concrete and short examples. As a real-life example, we examined a retirement plan and the progression of our savings in time. However, numerous other situations can be modeled after sequences or series and be studied by applying vectors or complex numbers. These concepts and methods are widely used in physics, engineering, data science, and more. Linear algebra, that is, the study of vectors, matrices, and tensors, heavily relies on understanding the concept of geometry and vectors and appears almost everywhere in data science and when studying neural networks. Geometry and trigonometry, on the other hand, are explicitly used to model physical motion (in video games, for instance) and more advanced concepts in geospatial applications...