It was a pleasure to take that course and learn from one of the biggest experts in machine learning, Andrew Ng. I would recommend taking this course for anyone interested in learning basic/advanced machine learning algorithms on a fairly deep level and applying them to real-world problems. One caveat is that the theory behind some of the advanced algorithms covered in the class might not be fully explained, so if you want to learn the nitty-gritty details, do your own research. Anyway, the knowledge from that course will benefit you regardless of your domain since machine learning is expected to enter almost every field in the future.
I have completed weekly assignments given in that course and posted all of them here in this repository. You can take a look at them if you struggle when taking the course (but don't look at the solutions without trying to solve the problems on your own first, promise?)
Okay, let me be a bit specific with the contents. The course covers from basic learning algorithms such as linear regression, logistic regression to more advanced types such SVMs and neural networks. Unsupervised learning algorithms such as K-means, PCA, Anomaly Detection also covered in the last weeks of the class. Besides that, you will learn about how to evaluate machine learning algorithms and build ml systems (related concepts: Bias/Variance Trade-off, Learning Curves, Error Analysis, Ceiling Analysis, and much more).
Good luck in your learning!