In this chapter, we covered vector and matrix operations for linear algebra. We looked at advanced matrix operations, especially featuring dot operations. You also learned about eigenvalues and eigenvectors and then practiced their use in principal component analysis (PCA). Moreover, we covered the norm and determinant calculation and mentioned their importance and usage in ML. In the last two subsections, you learned how to convert linear equations into matrices and solve them, and looked at the computation and importance of gradients.
In the next chapter, we will use NumPy statistics to do explanatory data analysis to explore the 2015 United States Housing data.
