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

4.8
stars
48,341 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

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.

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.

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326 - 350 of 5,521 Reviews for Structuring Machine Learning Projects

By Gerald B

Feb 28, 2018

I enjoy Andrew's approach to managing AI projects. He hits on very real issues we encounter when young, enthusiastic scientists want to solve problems with ML, but get lost in the numbers and multitude of possible next steps to take top improve the quality of a model.

By Daniel F

Sep 28, 2020

Es un curso extraordinario, entrega muchos detalles que hacen ganar destrezas a la hora de entrenar nuestros modelos y el profesor Andrew Ng muestra mucho información sobre su experiencia. Creo que hace un gran trabajo y me siento afortunado de haber pddido hacerlo.

By NiuYaaa

Nov 8, 2017

This course is a little bit hard for me, because I never heard these concept before. But through this course, I got a lot of new ideas to start my own machine learning project, and use these knowledge in practice. Thanks for the nice course and I will keep learning!

By Serkan K K

Feb 2, 2022

Although the topics seem easy, they are very important. It contains important information on how you should spend your money, time and energy most efficiently. Important tips to get started 1-0 ahead when building a machine learning system. Be sure to check it out.

By Alejandro J M R

Sep 10, 2019

Esta fase del curso es de gran importancia para saber como encarar un proyecto de deep learning con sus mejores prácticas. El proceso iterativo de desarrollo es bien explicado y te permite construir la base de cualquier tarea de deep learning que vayas a emprender.

By Simhadri M

Jul 21, 2018

Though we learn different machine learning models to train, we might miss the logical key practices and struggle and waste lot of time while training to achieve better results. This course teaches those important practices to efficiently implement the ML projects.

By naima v

Nov 18, 2017

Very helpful course. This course is very helpful that I got to know the things beyond the technical details of the neural network. About selecting test and dev set distribution, transfer learning, error analysis , end to end deep learning etc. Thank you Coursera...

By 苏高生

Oct 21, 2017

I have learnt algorithms theory of machine learning and how to use them into data mining. However, I’m not good at ML strategy, so I can’t build a very well model. Through this course, I know many methods to improve models which I build before. Thank you very much!

By Mohd F

Jul 23, 2019

Amazing Course ... different structuring strategies involving Orthogonalization, Single number evaluation metric, Carrying out error analysis, and how Cleaning up incorrectly labelled data including Transfer Learning are Beautifully explained by Andrew Ng...KUdos

By Kunal N

Feb 17, 2018

The best part of this course was the "ML Flight Simulator" questionnaires (peacotopia, auto-driving). These real life examples and grading on that is the best thing. It helps you learn and interpret the concepts much nicer. I wish there were more of such examples.

By Anurag A

Sep 11, 2017

This course has been really insightful into how we should work with machine learning projects. True, that most of the ideas discussed here are not covered in normal university curriculum. Thanks a lot to Professor Ng for coming out with this really helpful course.

By AJAY G

Sep 27, 2020

According to me, It is a very good course. In this course, they have taught about what can be the best practice to handles errors in the projects. They have taken different scenarios and gave us what can be the best choice that you can take to handle the problem.

By Akshat J

Apr 11, 2020

Once a person has a knowledge of how to become a developer in the prevoius courses, this course gives you the knowledge to escalate from developer to a project architect. It teaches crucial techniques required to guide the training model to produce better result.

By Selim R

Nov 19, 2017

I feel this is an extremely important course for all aspiring deep learning practitioners: being good is not only about knowing the algorithms and architectures, but also how to best manage your time and choose the most promising avenues to explore. A rare course

By Prakhar P

Jun 22, 2021

This is an exceptional course which helps tremendously in deciding the strategy for Deep Learning projects. A course which is based on practical challenges we face during implementation of real ML problems and projects. Thank you Andrew Ng, this is very helpful.

By Fahimul H

May 31, 2020

This was a much needed course after the 2nd one in this specialization! This course provides a clear and essential picture that is needed during development stage as well as shows the scopes of applying different techniques/blocks that were taught in course two.

By Raul T

Oct 9, 2017

Shows methods for improving performance of deep learning setups in a time efficient manner. I like that deciding what to try next for improving a deep learning setup's performance is a recurring topic in Andrew Ng's courses. Knowing this can save a lot of time.

By Tolga B

Jun 23, 2020

Very well structured (pun intended) and informative course! Andrew makes a fantastic job transfering his knowledge to his students. I gained very much insight in general for prioritizing different tasks in machine learning projects and planing the future steps.

By Isaiah A

May 22, 2020

This class, although short, was very interesting and serves as a useful guide to aid in building a deep learning model from scratch. The quizzes are really interesting as well as it makes you think about a step-by-step process in your own model implementation.

By Jay G

Oct 6, 2018

A fantastic course. I believe the strategies presented are practical and powerful in actually developing a deep learning system. I very much look forward to encountering those first problems and using these strategies to guide my model development. Well done!!

By Gillian P

Mar 2, 2018

Great course covering topics that are not seen anywhere esle and are probably highly udnerestimated. The graded simulations are a very good idea that could be used in other courses as well. Professor Ng excellent performance is no surprise for anyone having fol

By Kyung-Hoon K

Nov 23, 2017

This strategic thinking for planning ahead will absolutely save tons of times of ML developers when building a real-world ML system. This course gave me a lot of thinking points, even without programming assignment. Thanks professor Ng, Mentors, and classmates!

By Matthias T H H

Aug 27, 2017

Excellent course on how to analyze errors of machine learning applications. This material provides good ways to improve the speed of iteration over any machine learning project.

This class is unique as professors rarely provide this kind of insights. Thank you!

By Nguyen M

Aug 5, 2021

At first, I don't think this is a too exciting course for me. But it turns out really helpful to learn more about how to debug your ML model and make it better. It introduces many ways and approaches to debug a ML model and the solution to the problems as well

By Mahmoud H

Jul 31, 2021

It is vital before diving in advanced concepts in ML or DL to learn about how to structure ML projects. This course paves the way to this fundamental hot topic. As usual, Andrew makes it easy and illuminated to clearly understand and develop your skill in DL.