# Regression Math: Using Gradient Descent & Logistic Regression

Math    |    Intermediate
• 13 videos | 1h 37m 14s
• Includes Assessment
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  Describe what gradients are used for Work through a calculation of an epoch Standardize and shape data for gradient descent Implement a single epoch • 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

• 3.  What Gradients Are Used For
• 5.  Setting up Data for Gradient Descent
• 6.  Defining an Epoch Manually
• 7.  Performing Gradient Descent Manually
• 8.  How Logistic Regression Works
• 9.  Computing an S-curve
• 10.  Viewing Correlations for Logistic Regression
• 11.  Splitting and Shaping Data for Logistic Regression
• 12.  Performing Logistic Regression with Gradient Descent
• 13.  Course Summary

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