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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