Would you recommend his original Coursera course or his new course here: https://www.deeplearning.ai/courses/machine-learning-special...?

> Statistical rethinking by Richard McElreath

Thank you for that reference! I'm definitely going to check it out.

If you are able and want to, are you able to elaborate on how you changed the trajectory of your career? I'm a software engineer who was originally wanting to be a mathematician, so I've always been wanting to get back to something a bit more analytical and quantitative.

> If you are able and want to, are you able to elaborate on how you changed the trajectory of your career?

I was in data analytics at a health insurance company in more of a BI and data pulling role. Taking his course gave me the confidence to start doing more machine learning related projects in my company eventually becoming the first person in the company to be called "Data Scientist" after developing multiple models that were making an impact on the business.

edit: answer original question

I would do the first and classic one on Coursera first and then newer deep learning. The original one gives you better grounding in basic ML stuff that is not as "sexy" these days but is still fundamental and used for solving many problems.

I was in a similar boat but on the analytics side wrt to maths. I also took courses at a local university in Math to build up my knowledge. Learning pure math at the college level, like learning to do proofs in front of people on a chalkboard -- I think is very difficult to do unless you take it in the context of a class.

I think you should still give it a go, it's never too late!

edit: answer original question

That course was absolute trash from my perspective - nothing clicked. I am not sure why it's so highly praised, despite covering fundamentals. Pacing and everything was off, and this is from someone with a ton of coding experience and stats background.

I had a real "click" moment with Statistic Rethinking (2nd Edition) too! I never really "got" Bayes' Theorem before (could use it to solve homework problems and stuff but never appreciated it) but somewhere in SR Chapter 2 the whole point of it really snapped into focus. Good book!