Structuring Machine Learning Projects

DeepLearning.AI via Coursera

Coursera based on 50,043 ratings

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Overview

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a...

Syllabus

ML Strategy

Streamline and optimize your ML production workflow by implementing strategic guidelines for goal-setting and applying human-level performance to define key priorities.

ML Strategy

Develop time-saving error analysis procedures to evaluate the most worthwhile options to pursue and gain intuition for how to split your data and when to use multi-task, transfer, and end-to-end deep learning.