Learner Reviews & Feedback for Structuring Machine Learning Projects by DeepLearning.AI
About the Course
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 m...
Top reviews
ED
Aug 22, 2020
Excellent start for digging into topics that are not taught nowhere else. The author books 'Machine Learning Yearning' is a great next read that goes deeper in some of the aspects, really recommended.
WG
Mar 18, 2019
Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.
1826 - 1850 of 5,739 Reviews for Structuring Machine Learning Projects
By Samarth G
•Jul 3, 2020
Fantastic Course... enjoyed the practical knowledge that Dr. Andrew gives in the videos...
By Jobin R
•May 23, 2020
an amazing course providing professional level insights on doing machine learning projects
By Reem
•Apr 26, 2020
Awesome to get all those tips and advice from someone with Dr. Ng's experience! Thank you!
By Tim W
•Dec 19, 2019
Thank Professor Ng for teaching us strategies that we could not learn from somewhere else.
By Dmitry S
•Oct 18, 2019
An extremely useful course that puts in place the best practices of ML project structuring
By ujjawal g
•Jun 11, 2019
This is a really god course that help us build models in real world by saving lot of time.
By Aniruddha S H
•Mar 23, 2019
Excellent course. Covers problems you your algorithm, how to identify them and solve them.
By Bogdan G
•Mar 12, 2019
Very useful course on how to treat train/dev/test sets, with very interesting assignments.
By Rajat M
•Jan 4, 2019
a very practical course about dealing with the day to day eperience in ml related projects
By Stephen R
•Aug 10, 2018
Bias and variance are tough to understand, but very important when in the real world. Thx!
By Koshlan M
•Aug 6, 2018
Excellent Actionable Advice on Strategic Approaches For Improving Machine Learning Models.
By Antoine R
•Apr 28, 2018
the simulators are a brilliant idea, so sad we can't practice after the course has closed!
By Siva H S
•Jan 23, 2018
Great Insights. Will be definitely useful for anyone who wants to implement deep learning.
By David H C
•Nov 23, 2017
I really enjoy this courses... theres a lot of good tips that you can put in practice ASAP
By Harshavardhan S
•Aug 20, 2017
An extremely useful course to practice, debug and build good deep learning based systems !
By Marcilio M
•Jun 11, 2024
Excellent condensed knowledge shared that would otherwise take years to learn and master.
By Ambrish K
•Jan 21, 2022
Concepts were explained in detail by Andrew sir. Quizzes were awesome. Loved this course.
By Ayush S
•Sep 30, 2021
Great course, if you find this course helpful then do check out the MLOps Specialization.
By Pei Y L
•Jan 8, 2021
Really thankful to Andrew and the crew for providing this invaluable learning experience.
By Raushan K
•Sep 23, 2020
It helps to rethink about my machine learning projects and how I can better structure it.
By Yeshwanth K V S
•Aug 9, 2020
This is the most important and useful segment (relatively) over the entire specialization
By Сычев Д А
•Jun 23, 2020
Great Thanks to Anrew for running such courses and providing real expirience to everyone!
By NUKALAPATI S U
•May 24, 2020
I am glad that I got a chance to learn more about machine learning with good instructors
By Renato d C M J
•Apr 27, 2020
Very practical tips and scenario cases. Things that lack in many courses around internet.
By Marco M
•Jan 30, 2020
Very good insights about how to optimize and debug real world deep learning applications.