Caveat: I cannot advise in great detail because I don’t know you well enough to assess your current level of knowledge. Also I am not a teacher or lecturer, so cannot construct a course plan, and definitely not something that fits any kind of timetable.
Instead, I would advise you to pick up minimal basics but in a way that makes your confident that you really understand them. Then start tackling the machine learning that is your goal  whenever you hit some part which you don’t understand, take a step back and research that topic. As long as you are not enrolled in some class where you have to submit work every week, there is no need to try and create your own complete course guide from the outset.
There is not much subject area in between basic algebra and linear algebra or linear algebra and calculus. If you have basic algebra then both linear algebra and calculus are understandable. You only need the basics in each subject. E.g. in linear algebra you will need:
In calculus you will need:

What a derivative is, and what a partial derivative is

The “chain rule” or how to take a derivative when you have a function of a function
In stats you will need:

Different types of average (mean vs median, geometric mean vs arithmetic mean)

What the variance, standard distribution and standard error are
That’s the basics you will need to know comfortably well in order to understand the theories and notation in machine learning. It doesn’t hurt to understand them deeper.