## Linear Algebra in Python: Matrix Inverses and Least Squares

*Linear Algebra in Python: Matrix Inverses and Least Squares*is the second part of the tutorial on

`scipy.linalg`

, following *Working With Linear Systems in Python With scipy.linalg*, published on Real Python.

Linear algebra is an important topic across a variety of subjects. It allows you to solve problems related to vectors, matrices, and linear equations. In Python, most of the routines related to this subject are implemented in`scipy.linalg`

, which offers very fast linear algebra capabilities. In particular, linear models play an important role in a variety of real-world problems, and`scipy.linalg`

provides tools to compute them in an efficient way. In this tutorial, you’ll learn how to:

- Study linear systems using determinants and solve problems using matrix inverses
- Interpolate polynomials to fit a set of points using linear systems
- Use Python to solve linear regression problems
- Use linear regression to predict prices based on historical data

**[Link to the tutorial]**

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