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Comentários e feedback de alunos de Structuring Machine Learning Projects da instituição deeplearning.ai

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44,613 classificações
5,051 avaliações

Sobre o curso

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization....

Melhores avaliações

MG
30 de Mar de 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
22 de Nov de 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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251 — 275 de 4,996 Avaliações para o Structuring Machine Learning Projects

por Hiep P

14 de Dez de 2017

In the bloom of Deep Learning/Machine Learning industries, know how to build a project is more important and a priority to know what knowledges to build that project. Break the problems, take each step follow the guide and avoid common pitfalls in process, this course will satisfy you.

por Javier H E T

30 de Abr de 2020

this is definitely the best course i had taken. it has just 2 weeks, but it was the hardest. i will definitely come back to see the teachings here explained to check up if i'm thinking correctly so i don't make much mistakes in taking a direction in projects.

definitely recommended!!!

por Elena P

1 de Set de 2017

The case study format for quizzes was highly effective in helping me uncovering gaps in my knowledge that I didn't know were there. I would have liked to see at least one more case study per week. One per week just wasn't enough.

Overall good course with a few minor video glitches.

por Carlos A B R

22 de Jul de 2019

I found this course really interesting because it gives many details on what path to follow to achieve better results not only depending on the amount of data we have but also taking into account some small details that can make a difference when starting machine learning projects.

por Dharam G

2 de Jul de 2018

A very well systematic approach explained, to structure ML projects.Can be grasped and implemented by anyone, let it be a beginner or some expert.Really liked the idea of case study in quiz. (Wait ! How about extending this idea into some coding exercise ? Would be some real fun !)

por Andrew M

10 de Out de 2017

There is no coding in this course, but you learn a lot of how to design a Deep Learning Study. I learned a lot about the distribution of Training/Dev/Test sets and how to diagnose problems when a neural network is not performing as well as anticipated or if it is performing well.

por Tyler K

27 de Ago de 2017

Outstanding course. Many of the points made in this course mirror the hard earned knowledge I gained back when I worked on Dynamic Rank search engine focused neural networks.

This may end up being my favourite of the 5 courses but let's see if the last two have more math first. :)

por Alexios B

20 de Ago de 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.

por Brad M

22 de Ago de 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.

por WALEED E

17 de Jan de 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.

por Kanishk S

27 de Jun de 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!!!!

por Aditya V B

18 de Mai de 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.

por Debojyoti R

30 de Abr de 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.

por Maksim P

26 de Abr de 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.

por Karthikeyan R

19 de Dez de 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.

por Douglas H H H

22 de Set de 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

por Wade J

25 de Fev de 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.

por Armin F

15 de Mar de 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.

por Zifeng K W

22 de Ago de 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.

por Tristan A

14 de Jul de 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.

por Kai-Peter M

28 de Out de 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.

por Burhan A

22 de Jul de 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.

por Robin S

8 de Jan de 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!

por Marc S O

5 de Jul de 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.

por Pedro J

18 de Ago de 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