Advice on transitioning from Andrew Ng's Stanford Coursera Machine Learning (in MATLAB) into Python?
There are so many ways to expand your horizons on ML/DL. Currently I share the same conundrum, so to speak. 


In my opinion it is not necessary. Since you have completed the course, if you are confident about the material and know how each algorithm works, re-implementing them on another tool would not be that helpful since there are existing frameworks that you can use. On the other hand if you are not that confident, re-implementing everything on a lower level  (not via APIs) can serve as a second learning experience, making you more fluent in python and more familiar with tensorflow. So, pick a scenario in regards to your case. For me a combination seems to work. I find papers along with other theoretical material and then try to apply it via the frameworks on projects that I like.
The greatest source of projects on any level is Kaggle. You can start with the Titanic competition, choose an algorithm that you like and explore what can be done. Make your submission and then head to another kernel that is more to your liking. You can also study certain pipelines on existing kernels that worked for other people. Considering concepts and methodologies, I would recommend an approach where you study each and every tool that you use and really understand the math behind it. There is no one-pipeline/framework solves all problems (No Free Lunch Theorem). 


You also mention the deeplearning.ai specialization, it's a great source of information and Andrew Ng gives you intuition on aspects that are not very obvious when studying on your own. Another thing that helped me a lot is talking with people in the industry. Finding a mentor who inspires you and can give you his/her input on such questions is the best way to move forward. Go to AI/ML meetups and share your interest with other people, get to know companies in your area or even start an internship! 

Good luck with your journey!