Some of the questions answered in this course…

• Learn best practices from Google experts on key machine learning concepts.
• How does machine learning differ from traditional programming?
• What is loss, and how do I measure it?
• How does gradient descent work?
• How do I determine whether my model is effective?
• How do I represent my data so that a program can learn from it?
• How do I build a deep neural network?

Machine Learning Crash Course does not presume or require any prior knowledge in machine learning. However, to understand the concepts presented and complete the exercises, we recommend that students meet the following prerequisites:

You must be comfortable with variables, linear equations, graphs of functions, histograms, and statistical means.

You should be a good programmer. Ideally, you should have some experience programming in Python because the programming exercises are in Python. However, experienced programmers without Python experience can usually complete the programming exercises anyway.