Course · Intermediate

Gradient Descent Modeling in Python

In this course, you'll learn about gradient descent, one of the most-used algorithms to optimize your machine learning models and improve their efficiency. You'll discover the different types of algorithms, and you'll learn how to train models with stochastic gradient descent (SGD) using the scikit-learn library in Python.

  • Intermediate friendly
  • 3 hrs
  • 3 lessons
  • 1 project
  • Premium

Course overview

Optimize machine learning models by implementing and applying gradient descent techniques to efficiently train and improve predictive performance.

What's inside

3 lessons · 1 project

  • 01
    Understanding Gradient Descent

    <p>Begin developing the skills to code a gradient descent algorithm, step by step.<br></p>

    53 min
  • 02
    Implementing Gradient Descent in Python

    <p>Learn to code a basic Gradient Descent algorithm in Python. In this lesson, we will code a gradient descent algorithm to find the best fit line in linear regression, from loss function to printing and plotting our results.</p>

    38 min
  • 03
    Guided Project: Stochastic Gradient Descent on Linear Regression Project

    For this project, we'll step into the role of data scientists aiming to predict the optimal time to go to the gym to avoid crowds. We'll build a stochastic gradient descent linear regression model using Python.

    24 min
  • 04
    Gradient Descent in Scikit-Learn

    <p>Learn about some of the limitations of the basic gradient descent algorithm, as well as a potential solution. Learn how to use gradient descent with scikit-learn to make predictions.</p>

    42 min

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Aaron Melton
Aaron Melton
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