recommendations for self-study in ML/Deep Learning/the underlying math I am a beginner in ML, though I have completed Andrew Ng's Machine Learning course on Coursera and am soon to complete his Deep Learning specialization, also on Coursera. I'm looking for recommendations for things to study after that.

They need to be on the easier side since I am still learning. Also in addition to resources on ML or Deep Learning, I'm also interested in any resources on the underlying math. I have a book in probability and another book in calculus, though both are fairly dense. Anything lighter might be a plus.

Are there any resources you would recommend? I'm particularly interested in other courses on Coursera since that's what my company currently sponsors, though I'm interested in whatever suggestions you have to offer.
If you're looking for an introduction to mathematics for data science, take a look at the Coursera course Data Science Math Skills.