In section 4, you will learn about gradient descent, an optimization technique that is often used in machine learning. We cover the theory of gradient descent using videos and interactive jupyter notebooks. Afterward, we discuss different variants of gradient descent and how it’s used in practice. Finally, we discuss a more advanced classification model, logistic regression, on which we apply gradient descent for training.

After this section you can:

• • Explain the basics of iterative training and gradient descent
  • Explain the three different variants of gradient descent and their trade-offs
  • Tune the parameters of a gradient descent optimizer
  • Explain why feature scaling is important for gradient descent
  • Explain why logistic regression is better suited to classification than linear regression