If the only math you know is up to trig, you're probably multiple years away from getting caught up on the math.
You need to first learn calculus and linear algebra, and learn them very well. I would also recommend having a good understanding of probability. Learning all of these well will take at least a year, if not longer. For instance, I took one year of calculus in high school and then one semester each of linear algebra and probability, which that adds up to two years.
You'll need calculus so you can do optimization (i.e. at the simplest level, take a derivative, set it to 0, and solve. Of course there's more you can do with calculus in Machine Learning). You'll need linear algebra for almost everything in Machine Learning. Lastly, probability will be useful for understanding very basic methods like Naive Bayes[0]. There are other methods built on probability also[1].
If you skimp on learning any of these, you will never be able to understand Machine Learning at a deep level, much less even a shallow level.
You need to first learn calculus and linear algebra, and learn them very well. I would also recommend having a good understanding of probability. Learning all of these well will take at least a year, if not longer. For instance, I took one year of calculus in high school and then one semester each of linear algebra and probability, which that adds up to two years.
You'll need calculus so you can do optimization (i.e. at the simplest level, take a derivative, set it to 0, and solve. Of course there's more you can do with calculus in Machine Learning). You'll need linear algebra for almost everything in Machine Learning. Lastly, probability will be useful for understanding very basic methods like Naive Bayes[0]. There are other methods built on probability also[1].
If you skimp on learning any of these, you will never be able to understand Machine Learning at a deep level, much less even a shallow level.
[0] https://en.wikipedia.org/wiki/Naive_Bayes_classifier
[1] https://en.wikipedia.org/wiki/Graphical_model