Don't waste time like I did in my ML career
Before you start another course read this
STOP. You probably have 15 different ML textbooks on your laptop and 20 bookmarked courses.
Before you start one of them again, you should think about what you really need. In fact, even before that just think about what you really want from your career.
If you want to be a theoretical NLP researcher then it might make sense to finish that linguistics textbook but if you just want to be "smart enough" to work in ML then for the love of God PLEASE do not start another theoretical course.
Theory vs application
ML is not all theory. It's easy to think that you can just read all the textbooks available on DL and go through every single course out there but ask anyone actually working in ML and they will quickly tell you that it is unnecessary to do read more than 1 or 2 theoretical textbooks / go through more than 1 or 2 theoretical courses.
ML by itself is just a sub topic within CS, similar to automata theory, tree algorithms, and cryptography. When applied though, it's a powerful tool that brings in billions of dollars of revenue to the biggest companies and startups alike.
The scientists working on the models that power these ML applications have more often than not gone through many years of schooling as they honed their craft in doing research, but there are also a large amount of people that were just regular software engineers that ended up working in the ML space such as yours truly.
The speed run guide to ML =/= more courses
If your goal really is to just have all the theoretical knowledge in the world about ML then sure go read all those 10 textbooks you have saved on your laptop (although there will be lots of overlap). However, if your goal is to become someone who works on ML services then just finish 1 textbook or a single course on the foundations of DL and then start applying that knowledge on top of the software engineering skills you already have.
MLEs just apply ML to software applications. That's it. No black magic and no crazy scientist shit.
I wasted sooo much time reading and rereading textbooks on the same topics over and over again but at the end of the day, my true acceleration in my ML career came from my applied work of building and scaling large ML systems that often times used the most basic classical ML models such as random forest models.
The theory knowledge definitely helps in conversations with science teams and understanding business requirements, but the rest of the work comes from applied knowledge.
Anon, you are smarter than you think. Don't pick up another theoretical course and instead if you are going to go through some learning resource, look for applied courses/resources.
Here are some cool applied resources I can vouch for:
If you want to be a good MLE then make sure to check out my email list cuz they heard it all first: https://list.raymondyoo.com