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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

This chapter gave you a brief introduction to the branch of mathematics regarding probability theory.

We defined the concept of probability, as well as some of its most important rules and associated concepts such as experiment, sample space, and events. We also defined the very important concept of random variables and provided examples of the two main discrete and continuous random variables. Later in this chapter, we learned how to create random variables using the `scipy.stats` module, which we also used to generate the probability mass function and the probability density function. We also talked about two of the most important random variables in the (literal) universe: the normal distribution and the binomial distribution. These are used in many applied fields to solve real-world problems.

This was, of course, a brief introduction to the topic, and the goal was to present and make you familiar with some of the basic and foundational concepts in probability theory...