Finally, we can conclude that mathematical subjects such as linear algebra are the backbone for all machine learning algorithms. Throughout the chapter, we have focused on essential linear algebra concepts to improve you as a data professional. In this chapter, you learned a lot about linear algebra concepts using the NumPy and SciPy subpackages. Our main focus was on polynomials, determinant, matrix inverse; solving linear equations; eigenvalues and eigenvectors; SVD; random numbers; binomial and normal distributions; normality tests; and masked arrays.
The next chapter, Chapter 5, Data Visualization, is about the important topic of visualizing data with Python. Visualization is something we often do when we start analyzing data. It helps to display relations between variables in the data. By visualizing the data, we can also get an idea about its statistical properties.
