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

4.8
stars
48,871 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.

TG

Dec 1, 2020

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

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476 - 500 of 5,575 Reviews for Structuring Machine Learning Projects

By Girish G

May 3, 2020

This particular course details all the minute aspects needed to have a better model. All the concepts were explained clearly in the course. I felt this course to be a like a "icing on the cake" to basic Neural Network course.

By Gustavo E Z

Mar 6, 2020

Once more Andrew is greaqt teachng and very clear in his explanations. This course let me learn how to improve the development of a Deep learning project aiming at the right parameters and algorithms to be worked on the road.

By 吳沛燊

Aug 31, 2017

Very useful ! It is a common problem of getting lost in ML projects, although the guidance seems abstract at first glance, it proves to be invaluable when ever we are in the midst of struggling for better modeling performance

By Rashmi S

Jan 24, 2022

An Excellent Course to go with, will let you clear all your basic problems over distributions of Dev/Test sets, will make you understand and learn on avoidable bias inputs and choosing your parameters accordingly. Great one!

By Gudivada R K

Jun 21, 2020

Much needed course for those who are in their starting/middle stages of DL/ML projects. This course gonna play a vital role in their projects. The explanation from Andrew Ng was interesting with real-time scenarios examples.

By Gourav K

Aug 22, 2019

Thank You, Professor Ng, for creating so much valuable learning. The values to those are added and we get ambitious and inspired being through the interviews you took with great Deep Learning and Machine Learning scientists!

By David C

Jul 11, 2018

I came into this course with the bias that it would be the least applicable of the five in the series-- however, I really feel that the information conveyed was extremely important for practical application of deep learning.

By Salim L

Mar 25, 2018

Really helpful project strategy for Deep Learning that can save many months of work. While this course is a bit repetitive at times, Andrew Ng's recommendations are hugely important and his simulation tests quite innovative.

By Marko N

Aug 26, 2020

Pretty interesting ideas on how you can improve your deep learning system. It teaches you a number of strategies that help you identify the most promising things to try. Quizzes are especially interesting in this course.

By Sanket D

May 25, 2020

In depth learning of most sought and required concepts and giving insight on how to structure a ML project from scratch practically. The quizzes are just wow! They give a very good insight of how ML projects are structured!

By Justin K

Apr 27, 2020

Short course with no programming exercises, but full of good information that is immediately useful such as where your time will be best spent depending on situations you're likely to encounter in pretty much every project.

By Aloysius F

Mar 20, 2020

Excellent, this really goes into the nuance of successfully executing a project. Setting up an initial system is not that difficult. Understanding the sources of error a systematically resolving requires judgment and graft.

By amin s

Jul 26, 2019

This course is great. Recommend it to anyone working on Deep Learning projects. Saved me lots of time, and taught me how to systematically think about my problem and opened new windows to improve my network. Thanks, Andrew!

By Sean C

Feb 15, 2018

This was a valuable stepping stone in applying Andrew Ng's other teachings to realistic scenarios. The "simulators" were actually a great representation of realistic machine learning project issues & potential resolutions.

By Kurt K

Nov 27, 2017

A clear explanation of a difficult subject with an emphasis on being able to create and to understand your own neural networks.- Plus in this module how to allocate your resources so you can achieve a successful project.

By Asad A

Sep 2, 2019

Really good insights into the practical aspects of structuring projects. Large scale deep learning/ ML is as much about people management and strategic prioritization as it is about complex algorithms and big data handling

By Arvin S

Mar 10, 2018

This is a very useful course since that you can get an impotant instruction to build your own project. You can reduce your time cost and iterate quickly to produce more value by using the knowladges taught by this course.

By arulvenugopal

Jan 8, 2018

Good. However, understanding the importance of strategy, either additional scenario quiz (the simulation type quiz is good) or a programming assignment would reinforce the understanding (given short duration of the course)

By Heidi V B

May 17, 2020

I loved the translation of all the different succesfactors to the daily practice and examples in the course. It gave me an general idea of what to look out for when identifying my own AI problems and defining a NN for it.

By Abhishek R

Sep 15, 2019

This was probably the most useful course of the entire specialization with real-world examples, tips, tricks and techniques on how to approach the problems in Machine Learning world as a whole and Deep Learning in general

By Francisco R

Sep 28, 2017

Even though it's a short course and it doesn't have programming assignments, which I love doing, it has though these case study, which are quite fun and educative, helping you to get started in a Machine Learning project.

By Andrés S

May 24, 2020

I liked this course because I gave me an idea of real situations I could face working on Machine Learning, but I think a little code would've been helpful, for example, to better understand how to do a transfer knowledge

By Ladislav Š

Oct 20, 2019

This part of Deep learning specialization is similar to Machine Learning Yearning written by prof. Andrew Ng. I read the whole book and for me this was mostly a repetitive information - however, very useful and relevant.

By Shishir V

Nov 23, 2020

a lot of value for the minimal time invested, and the case study approach was the main reason I would give it 5 stars. Some parts in the videos could be fleshed out more with more real world examples where it was vauge.

By Naresh K P

Jul 25, 2020

This course helped me understand how to prioritize problems that we encounter in Machine Learning space. On the surface this might look simple, but I think this course will have a huge impact as I implement ML problems.