From the FAQ on the course's page:

"Programming (expected): intermediate Python programming skills: work effectively with loops, control flows, data structures, files, functions and OO programming. Prior experience with PyData libraries is also recommended (e.g. Numpy, Pandas, Matplotlib)Mathematics (recommended): Matrix vector operations and notation.

Machine Learning (recommended): understand how to frame a machine learning problem including how data is represented, how models are evaluated on the task and against each other, and how to optimize model performance for the best evaluation."

The FAQ answers this:

" What background knowledge is necessary?

Programming (expected): intermediate Python programming skills: work effectively with loops, control flows, data structures, files, functions and OO programming. Prior experience with PyData libraries is also recommended (e.g. Numpy, Pandas, Matplotlib)Mathematics (recommended): Matrix vector operations and notation.

Machine Learning (recommended): understand how to frame a machine learning problem including how data is represented, how models are evaluated on the task and against each other, and how to optimize model performance for the best evaluation."

This is described as a beginner specialization, so you're probably fine if you're comfortable with Python and have a passing familiarity with linear algebra.

The older machine learning class alone is eleven weeks, compared to nine weeks for the first three classes of this specialization. You might consider going through the first four weeks of that class, which lays the foundation for neural networks by introducing linear and logistic regression.