Vicknesh

“I wouldn’t be where I was without Dataquest. Be confident in what you learn with Dataquest. If you take the time to learn, you will be one of the few people in the world that can do what you do.”

Vicknesh Mano

Data Science Manager @Accenture

Course overview

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.

Key skills

  • 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

Course outline

Gradient Descent Modeling in Python [4 lessons]

Understanding Gradient Descent 1h

Lesson Objectives
  • Find the minimum value in arrays.
  • Train machine learning models with gradient descent.
  • Write simple algorithms for finding minimum values.
  • Code the basic building blocks of a gradient descent algorithm.

Implementing Gradient Descent in Python 1h

Lesson Objectives
  • Test how learning rate affects Gradient Descent
  • Build a basic Gradient Descent algorithm in Python
  • Code a Basic Gradient Descent function

Gradient Descent in Scikit-Learn 1h

Lesson Objectives
  • Identify the limitations of the basic gradient descent algorithm
  • Compare basic gradient descent and stochastic gradient descent
  • Make predictions with gradient descent using scikit-learn
  • Create a classifier with gradient descent

Guided Project: Stochastic Gradient Descent on Linear Regression 1h

Lesson Objectives
  • Explore a new dataset
  • Prepare the data to build a model using SGDRegression
  • Measure the efficiency of the model

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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Aaron

Aaron Melton

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