Ans: Four key ideas—statistics, linear algebra, probability, and calculus—are the foundation of machine learning. Calculus aids in model learning and optimization, even if statistical ideas are the foundation of every model. When working with a large dataset, linear algebra is especially helpful, and probability aids in the prognostication of future events.
Ans: Elementary calculus is more difficult than linear algebra. Unlike linear algebra, calculus allows you to get by memorizing algorithms rather than understanding the reasoning behind theorems.
All queries can be answered by grasping the linear algebraic theorems. Calculus is an exception, and even with strong theoretical background, practical questions can be exceedingly challenging.
Ans: Data Science & ML require using linear algebra as a fundamental technique. Beginners who are enthusiastic about Data Science should therefore become familiar with key linear algebra principles.
Ans: Many fields of computers, notably graphic, image recognition, cryptography, ML, machine vision, optimizations, graph algorithms, quantum computation, computational biology, retrieval of information, and online search, rely heavily on the notions provided by linear algebra.