For a blog, check out https://github.com/fastai/fastpages.
For courses to start with, there’s 2 I would recommend as a starting point. You could start with 1 or the other, or both. Here’s the courses and you can decide. Ultimately, you probably want to do both eventually so you are more well rounded.
Intro to Machine Learning - This is a gentler introduction. It mostly covers Random Forests and Decision Trees. While it’s an introductory course, it’s not the kind where you learn a bunch of trivial stuff that nobody actually uses. You will be learning things that I use in my job as a data scientist all the time You will learn why to have training vs validation sets, how these algorithms work, and more importantly code and apply this to real problems. A lot of the methods and techniques in the course are really good for tabular data (ie doing machine learning on data stored in SQL tables). There’s a lot more there, but I think that’s why you’d do that course. I don’t think there’s a book that goes with it.
Practical Deep Learning for Coders - This move a bit faster as it goes, but it is still a course you can do to start with if you want. You will be doing deep learning and learning more cutting-edge stuff. The biggest reason to start here in my opinion is if you just think Neural Networks are cool. I don’t think there’s a bad decision in which you go with. A new version of the course along with a new book and a new version of the library are being released in the next month, so it may make sense to work on the intro to machine learning for the next month. Then you can start fresh on this course with the new tools and best practices. Book can be found at github.com/fastai/fastbook currently, but you should definitely buy it at https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527/ref=sr_1_2?dchild=1&keywords=deep+learning+for+coders&qid=1593261408&sr=8-2v