Structuring Machine Learning Projects


Here one learnd to build a successful machine learning project. This course teaches AI aspirants how to effectively steer real-world projects to success.

Much of this content has never been taught elsewhere, and is drawn from Andrew’s experience building and shipping many deep learning products. This course also has two “flight simulators” that lets one practice decision-making as a machine learning project leader. This provides “industry experience” that one might otherwise get only after years of ML work experience.

Students of this course are able to:

  • Understand how to diagnose errors in a machine learning system, and
  • Prioritize the most promising directions for reducing error
  • Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
  • Apply end-to-end learning, transfer learning, and multi-task learning

The principles taught in the course are fundamental to solving ML problems effectively and many teams often spend months due to errors which are avoidable followings the suggestions in this course.

This is the third course in the Deep Learning Specialization.

Aditya Jyoti Paul
Aditya Jyoti Paul
Computer Vision and Image Encryption Researcher

My work makes machines smarter, secure and more accessible. I’m passionate about research, teaching and blogging. Outside academia, I love travel, music, reading and meeting new people!