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DeepLearning.AI

Structuring Machine Learning Projects

DeepLearning.AI and Stanford University via Coursera

Overview

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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 standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience.

The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Syllabus

  • ML Strategy
    • Streamline and optimize your ML production workflow by implementing strategic guidelines for goal-setting and applying human-level performance to help 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.

Taught by

Andrew Ng

Reviews

4.3 rating, based on 6 Class Central reviews

4.8 rating at Coursera based on 49594 ratings

Start your review of Structuring Machine Learning Projects

  • The course covered a range of practical issues, such as creating a single performance metric to quickly compare algorithms, how to compare the algorithm with human error to estimate Bayes (ideal) error rates and how to manually inspect and analyze e…
  • Anonymous
    Wow, that is quite informative. I like this article very much. The content was good. If any of the engineering students are looking for a projects for machine learning projects for students, I found this site and they are providing the best service to the engineering students regarding the projects machine learning projects for students
  • Profile image for DIGITAL MARKETING
    DIGITAL MARKETING
    That is quite informative. I like this article very much. The content was good. If any of the engineering students are looking for a projects for deep learning projects for final year, I found this site and they are providing the best service to the engineering students regarding the projects deep learning projects for final year.
    https://takeoffprojects.com/deep-learning-projects-for-final-year
  • Anonymous
    Thanks for the great post you posted. I like the way you describe the unique content. The points you raise are valid and reasonable. If any of the final year students are looking for the deep learning final year projects.
    https://takeoffprojects.com/deep-learning-final-year-projects
  • Silveira Homero
    This course follows Neural Networks and Deep Learning and Improving Deep Neural Networks. the practical tips are sure valuable to the practitioner.
  • Profile image for Raivis Joksts
    Raivis Joksts
    Interesting course, if you could call it that (more like a series of experience sharing videos) with some applicable ideas. Could benefit from coding exercises that actually model the situations discussed.

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