Embark on a comprehensive 10-hour calculus course designed for beginners and machine learning enthusiasts alike. Delve into the mathematical study of continuous change, exploring crucial concepts with in-depth explanations. Begin with an overview of calculus before progressing through topics such as limits, continuity, derivatives, and their applications. Master differentiation rules, rates of change, and trigonometric functions. Explore advanced concepts including the chain rule, implicit differentiation, and partial derivatives. Apply your knowledge to real-world scenarios through related rates and optimization problems. Conclude with an introduction to antiderivatives, equipping you with a solid foundation in calculus principles essential for further study or application in machine learning.
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
