Machine learning revolves around algorithms, which are essentially a series of mathematical operations. These algorithms can be implemented using various methods and in numerous programming languages, but their underlying mathematical principles are the same.
A common argument is that you don't need to know mathematics for machine learning because most modern libraries and packages abstract the theory behind the algorithms.
However, I would say that if you want to become a high-level machine learning engineer or data scientist, you need to know at least the basics of linear algebra, calculus, and statistics.
Of course, there is more mathematics to learn, but it is better to start with the basics and you can always enrich your knowledge later.
You don't need to understand all of these concepts at a master's level, but you should be able to answer questions like what is a derivative, how to multiply matrices, and what is maximum likelihood estimation.
That list I just wrote is the foundation of almost all machine learning algorithms, so having this solid foundation will set you up for long-term success.
Some of the key things I recommend you learn are: