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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

# Operations on Multiple Matrices

We will now be performing operations between two or more matrices and see the functions that will help us to achieve that. We will be covering how to write an inverse of a matrix, logical operators, dot products, eigenvalues and eigenvectors, outer products, and the determinates of a matrix.

It should be noted that there are plenty of other things you can do with matrices, and the official NumPy documentation is a really good resource for referencing information according to the requirements of the user. Most of the topics that we are going to cover are part of the linear algebra package of the NumPy library. There are far wider applications in physics and mathematics that are beyond the scope of this chapter for each of the topics we are going to cover, but it should suffice to know that they all play a very important role in understanding matrices.

Note