Update: sorry, when I wrote the following, I (obviously) missed your intermediate post where you said you were experienced in python and gave your proposed roadmap. Please just consider this as a general map.
Do you already have programming experience including in python? If not, then you need to take a python course first. All the courses here assume python proficiency.
Do you know enough Linear Algebra to know how matrix multiplication (dot product style) works and are comfortable working with vectors and matrices? If not, then you should take at least Course 1 of Mathematics for Machine Learning (M4ML) as your next step after learning python (if required). Course 2 and Course 3 of M4ML cover calculus and probability and statistics. M4ML is new and I haven’t taken it yet (I already have a math degree), but most of the intermediate curriculum is designed such that you don’t need calculus or statistics knowledge (for DLS e.g.), but knowing those will help your intuitions.
If you already have both python and linear algebra in your toolbox, then you can start with MLS as Saif has suggested and then you’ll definitely want to take DLS next after that. But you could also just jump directly to DLS if you have the math and programming. DLS does not assume any previous ML knowledge. MLS is more introductory and will give you exposure to other types of ML algorithms besides neural networks, but DLS is really the core of the intermediate curriculum here. It gives you a thorough introduction to all the main types of Deep Neural Networks: Fully Connected Nets, Convolutional Nets and Recurrent Nets (Sequence Models).
Once you’ve mastered DLS, then you have a number of options. You can explore the various TensorFlow courses to learn more about how to apply TF. You may also want to then take some of the more domain specific courses as Saif mentioned, e.g. AI for Medicine or Natural Language Processing. The GANs Specialization is also very interesting and not really domain specific.
That should be enough to get you started and as you learn you will certainly develop your own curiosity about which directions are the most interesting to pursue further.
I hope you will enjoy your learning journey!