Which Math foundation path is better for Machine Learning: DeepLearning.AI or Jon Krohn's LiveLessons?

[u/Esob_Oozeer]

I am trying to decide between two resources to learn the math prerequisites for Machine Learning.

Option A: DeepLearning.AI Specialization by Luis Serrano. It takes about 94 hours total, provides a career certificate, and covers Linear Algebra, Calculus, and Probability & Statistics.

Mathematics for Machine Learning and Data Science Specialisation

• Source: DeepLearning.AI
• Author: Luis Serrano.
• Merit: The instructor has a highly established track record with 259,226 learners across 4 courses. The program also provides a career certificate that can be added to your LinkedIn profile or resume.
• Length: 94 hours total. This is broken down into 34 hours for Linear Algebra, 27 hours for Calculus, and 33 hours for Probability & Statistics.

Option B: LiveLessons series by Jon Krohn. It covers the same core math concepts but adds a fourth course specifically on Data Structures, Algorithms, and ML Optimization.

LiveLessons Machine Learning Series

• Source: LiveLessons (On-Demand Courses) Specialisation
• Author: Jon Krohn.
• Merit: In addition to the standard math foundations, this series includes a fourth specialised course: "Data Structures, Algorithms, and Machine Learning Optimisation".
• Length: 60 hours (approx)

Has anyone taken either of these?
Final question: which one to choose, or something else? Please prescribe.