# Calculus: Derivatives with Linear and Quadratic Functions & Partial Derivatives

Math    |    Intermediate
• 10 Videos | 1h 22m 18s
• Includes Assessment
Linear functions change at a constant rate, and that in turn, makes the rate of change of a linear function a constant. This and other related insights into linear and other mathematical functions can be quantified using calculus. Through this course, you'll examine the steps involved in applying the differentiation operation to study a moving particle. You'll then understand how the partial derivative of a function that depends on multiple independent variables is computed with respect to one of those independent variables by holding all other independent variables constant. This course will also allow you to investigate how partial derivatives play a crucial role in the training phase of building a machine learning (ML) model. Upon completion of this course, you will be able to compute the partial derivative of a function that depends on multiple independent variables and better understand the training process of a machine learning model.

## WHAT YOU WILL LEARN

• discover the key concepts covered in this course compute derivatives of linearly changing functions using built-in functions outline derivatives and slope compute the velocity of a moving particle describe velocity and its relationship with trajectory
• outline how partial derivatives work create partial derivatives with multiple independent variables compute partial derivatives of different functions apply partial differentiation to training machine learning (ML) models summarize the key concepts covered in this course

## IN THIS COURSE

• 1.
Course Overview
• 2.
Calculating Derivatives on Linear Functions with Built-in Functions
• 3.
Interpreting the Derivative as the Slope of a Tangent Line
• 4.
Interpreting the Velocity of an Accelerating Particle
• 5.
Modeling Velocity and Trajectory
• 6.
Partial Derivatives
• 7.
Computing Partial Derivatives
• 8.
Performing More Partial Derivative Computations
• 9.
Training Neural Networks with Partial Derivatives
• 10.
Course Summary

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