Regression Math: Using Gradient Descent & Logistic Regression

Math    |    Intermediate
  • 13 videos | 1h 37m 14s
  • Includes Assessment
  • Earns a Badge
Rating 4.0 of 3 users Rating 4.0 of 3 users (3)
Gradient descent is an extremely powerful numerical optimization technique widely used to find optimal values of model parameters during the model training phase of machine learning. Use this course as an introduction to gradient descent, examining how it can be used in a wide variety of optimization problems. Explore how it can be used to perform linear regression, carefully studying the matrix equations used to compute the gradients and updating the model parameters using the gradients as well as the learning rate hyperparameter. Finally, apply a form of gradient descent known as stochastic gradient descent to fit an S-curve, thus implementing logistic regression on a data set. By the end of the course, you'll be able to assuredly implement logistic regression using gradient descent.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Outline how gradient descent works
    Describe what gradients are used for
    Work through a calculation of an epoch
    Standardize and shape data for gradient descent
    Implement a single epoch
    Perform gradient descent
  • Recall how logistic regression can be used for classification
    Calculate an s-curve in logistic regression
    Identify correlations for performing logistic regression
    Set up training and testing data for logistic regression
    Explore and perform stochastic gradient descent
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m
  • 7m 41s
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    3.  What Gradients Are Used For
    7m 35s
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    4.  Computing Gradient Descent
    6m 40s
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    5.  Setting up Data for Gradient Descent
    9m 17s
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    6.  Defining an Epoch Manually
    9m 35s
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    7.  Performing Gradient Descent Manually
    9m 53s
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    8.  How Logistic Regression Works
    6m 51s
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    9.  Computing an S-curve
    7m 51s
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    10.  Viewing Correlations for Logistic Regression
    9m 19s
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    11.  Splitting and Shaping Data for Logistic Regression
    8m 21s
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    12.  Performing Logistic Regression with Gradient Descent
    10m 1s
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    13.  Course Summary
    2m 10s

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