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Learner Reviews & Feedback for Structuring Machine Learning Projects by DeepLearning.AI

4.8
stars
49,631 ratings

About the Course

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....

Top reviews

AM

Nov 22, 2017

I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.

MG

Mar 30, 2020

It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.

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301 - 325 of 5,688 Reviews for Structuring Machine Learning Projects

By Alexios B

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Aug 20, 2017

This part of the specialization is short but it includes a lot of valuable information. Many of the tips are quite basic engineering best practices which most engineers should find natural, but some are very specific to deep learning and these are particularly useful to newcomers.

By Brad M

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Aug 22, 2019

This is truly some information you'll never get in a standard class setting; this is more similar to compiling years of ML experience into short packets of advice that will guide your decisions for years to come. Extremely helpful, and recommended for all deep learning engineers.

By WALEED E

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Jan 17, 2019

This course is really what any PhD would need to conduct his research in more time saving and efficient manner. It would be great if coding was accompanied (even if only running and watching results) to touch all aspects of analysis and suggested improvements could be visualized.

By George B

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May 13, 2021

Great course. I had a couple of ML courses at University, but nobody ever explained those concepts: orthogonalization, the data mismatch problem and what to do with it, different versions of human-level performance, end-to-end learning pros cons (everyone just talks about pros).

By Kanishk S

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Jun 27, 2020

To Andrew and team (mentors and organizers), I am glad I opted for this course! You guys give such great insight on approaching and solving a Deep Learning problem, I don't think I would have ever found a better introductory course on Neural Nets. Thank you so much, everyone!!!!

By Aditya V B

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May 18, 2020

One of the most important course in this series . This course actually helps you visualize the problems and standstills you might face when you are working on a model in real life. It also talks about practical solutions to improve your model that are valuable in the tech world.

By Debojyoti R

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Apr 30, 2020

An unique course. I don't think such a course is offered by any MOOC. I would suggest every DL enthusiast to take this course.

The programming assignments are very challenging. It forces us to think abstractly to find solutions encountered during real life Deep Learning problems.

By Maksim P

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Apr 26, 2020

Despite this course is labeled as basic level, it contiains very useful information related to strategy of developing ML projects. And use cases prepared by prof. Ng and his team is what you will get only by practice. It really helpful to structure what was learned by this day.

By Karthi K

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Dec 19, 2019

A great insight into how to improve the performance of the deep learning system without having to actually spend long hours/days and working on real project. Learnt a lot in improving the model's performance and where to look for the errors and how to invest time in debugging.

By Douglas H H H

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Sep 21, 2017

I totally agree with your flight simulator analogy. This really helps me to learn your experience in practising machine learning knowledge; which otherwise I need to spend many years of doing "try and error"

Thank you very much for your kind sharing of your practical experience

By Wade J

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Feb 25, 2018

As always, very well structured material considering the nature of the content and trying to make it understandable and make sense. I also appreciate that it is rooted in real-life experience which serves to make me pay really close attention to everything that is being said.

By Armin F

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Mar 15, 2020

This course teaches the trade off between Bias, Variance, Data Mismatch . You will learn how to split data and how to evaluate your model. It also covers error analysis systematically. It gives many examples of transfer learning, multi-task learning, and end-to-end learning.

By Zifeng K W

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Aug 22, 2017

Very refreshing to learn about also the more practical aspects of machine learning project like organising, structuring and executing the projects. The course definitely gives me more ideas now on what to do when starting a project and what to look into when facing problems.

By Tristan A

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Jul 14, 2020

Very useful guidelines for approaching projects! This topic is rarely addressed in comparison to the discussion of modeling techniques, however, in the real-world application, the trade-offs on where to start and how to proceed are just as important as the model themselves.

By Kai-Peter M

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Oct 28, 2019

Great course!!! The best online course I have ever taken! I enjoyed almost every day I participated in that course, really an educational treasure! It is so comprehensive and detailed at the same time. Due to the good presentation of the topics it was really understandable.

By Burhan A

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Jul 22, 2019

I have learned tremendous things about machine learning projects which I feel if I have not learned and started any machine learning project than it would have taken me many months or years to complete. Now i know how I could complete my project efficiently and effectively.

By Robin S

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Jan 8, 2019

I can only say that I am amazed how I much learned by just watching the few videos of this course. It is so short but still contains plenty of new information. It also helped me at work by giving me a deeper understanding of how to approach various problems. Awesome course!

By Marc S O

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Jul 5, 2020

This is a very good course that which should be helpful for project managers/leaders that tells on which direction should the machine learning team to go as it gives techniques and intuitions on how to decide on which direction should a certain machine learning project go.

By Pedro J

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Aug 18, 2019

This is an excellent course and I was able to understand with clear explanation, example and practice cases on how to improve the Deep Learning workflow in order to make the right decisions on what direction the team need to take to improve the DL model. Highly recommended

By kevin E

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Sep 10, 2018

I have decided to reserve 5 star ranking exclusively for Professor Andrew Ng. I did a course which was learning to learn which was quite good. A course by Prof. Andrew Ng titling "learning how to teach" would do tremendously in propelling the world of data science forward.

By Yedhu K V P

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Jun 8, 2018

I loved this course. Although there wasn't any exercise other that quiz, this was pretty interesting and gave me a lot of ideas to try. I was wondering about how transfer learning would work before this, and now I know how it works! I am looking forward to the next course.

By Vincenzo M

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Oct 28, 2017

This course confirms the capacity of Andrew Ng to teach complex topics in a simple way. The course is full of advices and trick to structure and to success with machine learning projects. Suggested for people that already took courses on machine learning and deep learning.

By DDSharma

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Nov 30, 2022

This is a great course even for ML/DL project managers and organizational leadership. Prf. Ng very clearly lays out various considerations ifor collecting and setting up data. The insights he shares would help going beyond the hype and on the path to a successful project.

By Ioannis K

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May 11, 2020

Having concluded the first three courses I have to note that in my opinion this is the most important course because it offers pure ml exprerience, something you cannot find easily. Moreover the simulators were excellent way to test your ability to apply all the concepts.

By Jacob S

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Jan 20, 2020

Even after working in the field for many years, I find that I learn something new in every video. Andrew really captures well what is important from both practical and theoretical perspectives and is a master at explaining concepts in a simple, but not dumbed down manner.