Linear algebra is essential in machine learning for representing data as vectors and matrices.

Probability and statistics are crucial in machine learning for understanding data patterns and making predictions.

Calculus is vital in machine learning for optimizing algorithms. It helps in understanding how model parameters should change to minimize errors.

Design programming and algorithms focus on creating efficient solutions to problems. It involves structuring code and selecting the best algorithms to achieve optimal performance.