If you are a full stack developer, there's no need to read entire books on Python and Git. Data visualization should not be new knowledge for a full stack engineer. Python can be picked up in a day, and there's no more git in ML than full stack web dev. You don't need to be able to produce good or even elegant code to do well in ML. Scientific programming skills trumps software engineering here. Don't get caught up in the weeds of step 1. Most of the linear algebra and multivariable calculus books are at a level of rigor that are beyond what's need for ML, especially ML engineering. Unless you are doing specific basic research in statistical learning you almost never need to prove your equations at a mathematical level.
I recommend taking a look at Murphy's probabilistic learning book to have a stronger foundation. If you want to do ML engineering or research beyond being a Pytorch code monkey, you have to understand the high level (heavy emphasis here, make sure whatever you are reading covers the Metropolis algorithm, marginalization, and graphical models, not just basic sampling/pop sci/EA rationality Bayes cultism) Bayesian statistics which defines the fields of variational and causal learning. Tricks in computer vision, natural language, speech, can be picked up from review papers and books as needed. Those verticals require experience more than formal training per se.
I also suggest doing a refresher on dynamics/differential equations and signal processing. Many CS education do not cover these topics well and they are heavily used in many areas of machine learning.
See my old ML reading list suggestion
https://news.ycombinator.com/item?id=34312905
Read lots of papers on arxiv and elsewhere to stay up to date on latest ML tricks and heuristics.
(Also as mentioned in another comment, Karparthys's Zero to Hero is an excellent intro to deep learning, but please don't stop there, learn the information theory and statistics behind how the technology works)