Calculus for Machine Learning and Data Science

I have completed the second course from the specialization Mathematics for Machine Learning. The specialization, available here, comprises three courses: linear algebra, calculus, and statistics, all applied to machine learning.

The second course, 'Calculus for Machine Learning and Data Science,' available here, proved invaluable in understanding the underlying mechanics of gradient descent and Newton's method, essential for optimization tasks. While the Deep Learning Specialization also covers these topics, I found this course to be more accessible for beginners in machine learning.

Additionally, the course provides a comprehensive overview of various libraries for derivative calculations, such as SymPy, NumPy, and JAX. It effectively explains the advantages of JAX over NumPy and SymPy.

Upon completion, the course offers certificates of completion, an example of which is available here. I wholeheartedly recommend this course, with a particular focus on advanced topics like JAX, forward and backward propagation, parameter updates, and other intricacies of neural network-based machine learning."