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.
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.
Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.
We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.
Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.
Build confidence with our in-depth projects, and show off your data skills.
Work with real data from day one with interactive lessons and hands-on exercises.
Share the evidence of your hard work with your network and potential employers.
