Gradient descent is one of the most commonly used optimization algorithms to train machine learning models, such as linear regression models, logistic regression, or even neural networks. It finds the minimum of any convex function by gradually converging toward it.
In this course, you’ll learn the fundamentals of gradient descent and how to implement this algorithm in Python. You’ll learn the difference between gradient descent and stochastic gradient descent, as well as how to use stochastic gradient descent for logistic regression.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll combine your new skills in a project to optimize a stochastic gradient descent algorithm on linear regression.
- Coding a basic gradient descent algorithm
- Understanding the basic batch and stochastic gradient descent uses
- Visualizing stochastic gradient descent using matplotlib
- Applying stochastic gradient descent in Python using scikit-learn
Gradient Descent Modeling in Python [4 lessons]
Projects in this course
Guided Project: Stochastic Gradient Descent on Linear Regression
In this project, you will load, explore, and prepare a dataset to build a stochastic gradient descent regression model (linear regression), and then you will measure the efficiency of the model and visualize the results.
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