Notes from DeepLearning.ai's course Mathematics for Machine Learning and Data Science taught by Luis Serrano from Coursera.
Although I was largely inspired by the course, I've also used other sources like Khan Academy, Wikipedia to name a few.
I've also included some extra content like PCA and SVM from scratch, the actual implementation of back-propagation (credit: Deep Learning Specialization , the relationship between Newton's method and Taylor series, the Lagrange multiplier method, etc.
I did not include notes on statistics because it's a topic I didn't need to refresh as much as linear algebra and calculus.