Calculus: Derivatives with Linear and Quadratic Functions & Partial Derivatives

Math    |    Intermediate
  • 10 videos | 1h 22m 18s
  • Includes Assessment
  • Earns a Badge
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.


  • 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


  • 1m 48s
  • 8m 18s
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    3.  Interpreting the Derivative as the Slope of a Tangent Line
    8m 53s
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    4.  Interpreting the Velocity of an Accelerating Particle
    8m 58s
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    5.  Modeling Velocity and Trajectory
    10m 57s
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    6.  Partial Derivatives
    7m 4s
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    7.  Computing Partial Derivatives
    11m 7s
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    8.  Performing More Partial Derivative Computations
    11m 3s
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    9.  Training Neural Networks with Partial Derivatives
    12m 25s
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    10.  Course Summary
    1m 45s


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