[RockyMcNuts]

Do what feels good for you :)

Ng is a fine place to start, you get some pretty quick wins, doing MNIST from first principles within a month or two. You just need to know or get comfortable with matrix multiplication. It strikes a reasonable balance between being rigorous and approachable for a committed student at an undergrad level.

Principles of Statistical Learning is easier https://lagunita.stanford.edu/courses/HumanitiesandScience/S...

LAFF linear algebra is just starting http://www.ulaff.net/

Hinton's Neural Networks is offered in the fall https://www.coursera.org/learn/neural-networks

For my money, I wouldn't do something like Practical Machine Learning in R, because I think you'll learn more R than machine learning. I wouldn't do the Udacity TensorFlow course because I think it assumes a lot of stuff you would learn in Ng's class ... I think Ng is a fine place to start.