In the context of machine learning, using linear algebra is only necessary for advanced optimization techniques and not for basic data manipulation tasks.

Linear algebra is the backbone of almost every step in machine learning, not just the fancy optimizers. Even when you simply load a dataset, you are arranging rows and columns in a matrix so you can add, multiply, or transpose it, and these operations rely on matrix algebra. For example, normalizing a feature column is a vector operation that uses dot products and norms, all of which are linear algebra concepts. When you train a model, you compute gradients that involve matrix multiplications, and when you evaluate predictions you multiply weight vectors by input vectors. Thus, linear algebra is essential for both simple data handling and for the advanced math that drives learning.