I would suggest an alternative approach. You don't seem to be really interested in the nitty-gritty of the algorithms than in actually knowing how to apply the

I would suggest an alternative approach. You don't seem to be really interested in the nitty-gritty of the algorithms than in actually knowing how to apply them to get the results you desire. Jumping in deep can really suck your time as you need to brush up on lots of fields. Also it will be sometime before you have anything meaningful to show, which can be quite frustrating. May I suggest a case by case approach wherein you take a problem and try to apply ml kit like scikit-learn to see which algorithm produces the results you desire and trying to learn why it does and how? You can find all the gaps in your knowledge as you learn and fill them on a need by need basis. This is from someone who tried real hard to do it the bottom to top way and found it frustrating to learn with a full time job and family. Now I am going to try the top to bottom way and focus on one niche. Bottom to top approach works better when you are at school where you have specific assessments to measure progress and the end of semester to signal the completion of each course/subject. Since I have just started this alternative approach, I do not have an exact map to propose. If someone has walked this path, any inputs would be most welcome.