Introduction

Welcome to Testing and Debugging in Machine Learning! Testing and debugging machine learning systems differs significantly from testing and debugging traditional software. This course describes how, starting from debugging your model all the way to monitoring your pipeline in production.

Image of the five phases of machine learning. The five phases are:
one, define a ML problem and propose a solution; two, construct a dataset;
three, transform data; four, train a model; and five, use the model to
make predictions. This course focuses on the
fourth and fifth: train a model and use the model to make predictions.

What this course does not cover:

Tensorflow Debugger: Specialized debugger for Tensorflow graphs.
Model understanding: Gaining insight into ML model behavior.
Guidelines for specific ML applications.

Prerequisites

This course assumes you have:

Completed Machine Learning Problem Framing or have equivalent knowledge.
Completed Machine Learning Crash Course or have equivalent knowledge.
Completed Data Preparation and Feature Engineering or have equivalent knowledge.
Basic programming knowledge in Python.

Happy Learning!

Overview