This course on Calculus for Beginners aims to teach learners the fundamental concepts of calculus with a focus on applications in machine learning. By the end of the course, students will be able to understand limits, derivatives, differentiation rules, optimization problems, and more. The course employs a comprehensive explanation method and covers topics such as trigonometric functions, chain rule, implicit differentiation, and antiderivatives. This course is intended for individuals interested in learning calculus from scratch and for machine learning enthusiasts looking to apply calculus concepts in their field.
Calculus for Beginners - Calculus for Machine Learning
Overview
Syllabus
) A Preview of Calculus.
) The Limit of a Function. .
) The Limit Laws.
) Continuity.
) The Precise Definition of a Limit.
) Defining the Derivative.
) The Derivative as a Function.
) Differentiation Rules.
) Derivatives as Rates of Change.
) Derivatives of Trigonometric Functions.
) The Chain Rule.
) Derivatives of Inverse Functions.
) Implicit Differentiation.
) Derivatives of Exponential and Logarithmic Functions.
) Partial Derivatives.
) Related Rates.
) Linear Approximations and Differentials.
) Maxima and Minima.
) The Mean Value Theorem.
) Derivatives and the Shape of a Graph.
) Limits at Infinity and Asymptotes.
) Applied Optimization Problems.
) L'Hopital's Rule.
) Newton's Method.
) Antiderivatives.
Taught by
Academic Lesson
