Chevron Left
Voltar para Structuring Machine Learning Projects

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

4.8
estrelas
45,314 classificações
5,160 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

TG
1 de Dez de 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.

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.

Filtrar por:

226 — 250 de 5,104 Avaliações para o Structuring Machine Learning Projects

por Fahad S

6 de Set de 2018

The content is very unique and extremely insightful in how to structure a machine learning project. As a machine learning practitioner, I can personally vouch for the usefulness of the suggestions made by Andrew NG. Had I known all of this before, it would have saved me a lot of time on numerous projects.

por Tushar M

16 de Mar de 2018

This is the best ML course I have taken so far. A lot of ideas around train/dev/test sets, bias variance trade-off and difference of data distributions between train and dev sets snapped into place for me. I am sure it will take me a while to internalize this content but I feel like I have found the path.

por Edward D

11 de Out de 2017

Brings a lot of useful insight of how to tune the model more from the data point instead of the model or algorithms. This could be super helpful in solving real world problems. Also the two case study homework helped me a lot to get a better understanding of what Andrew meant in his lecture. Great course.

por Shivam S

15 de Jul de 2020

The thing is to get started, sir Andrew has given huge insights in working of Neural Networks and driven us through the different parts of the journey. This is not just a course but a story that every Deep Learning enthusiast must go through to see the difference. Eye opening Experience.

Thank You

Andrew

por Smail K

3 de Jun de 2020

Another amazing course on deep learning and machine learning in general! This course gives you amazing insight into how you could strategize while running a machine learning project. I enjoyed going through the content of this course a lot, but not as much as the case studies! they seemed very realistic.

por Hari K

22 de Out de 2020

Very practical advice for a beginning deep learning engineer on what to do to avoid getting lost in the hyperspace of all the parameters one could change to train a better neural network model. I do wish however there was more explanation of why the different heuristics work, that Prof. Andrew suggests.

por Ashwin K

29 de Abr de 2020

Good practical tips for planning out your machine learning projects. Every machine learning engineer should check out this course as it will be really helpful in planning your machine learning projects and allocating time for tasks in the project. And as usual, great, lucid instruction by Andrew Sir! :)

por Carlos A L P

24 de Nov de 2020

Very interesting to see a transversal course of how to model and manage ML and DL projects, I am happy to learn new tricks to deal with train/dev/test sets with different distributions, dealing with small datasets and new techniques to apply transfer learning and lastly, how multi-task work in general

por J.-F. R

18 de Fev de 2020

Great course by Prof Ng. I had taken his Machine Learning course a few years ago, so expected high standards of content and assignment preparation - I was not disappointed. Staff is very responsive and helpful in forums as well. I highly recommend it. Taken as part of the DeepLearning specialization.

por Ayush K

19 de Jan de 2020

Amazing course where Andrew NG shares his advice on how to work with datasets of different distributions etc. Coming from such an experienced practitioner is so helpful.

The Quizes are really helpful as they deal with case study and really make you feel like you're in the spotlight

Loved this course!!!

por Zoheb A

5 de Fev de 2019

The two quizzes of this course were unique. Never came upon such a quiz in any other online course. Along with the videos and supplementary pdfs, this course was quite unique and important in every aspect. I will use the approach I learnt here on my next ML projects. Thanks to Andrew Ng and the team.

por Arturo H

15 de Jul de 2020

Really good course. As a machine learning practicioner I discover new ways to attack a machine learning problem. It taught me where should I focus to achive my goals faster. I think that in the exams they could give a little more explanation of why some answer is wrong. Overall an excellent course.

por João F

25 de Mai de 2019

Very good course. Professor Ng explains very well why some strategies are better than others and how a deep learning practitioner or team can save a huge amount of working hours by following the instructions taught in this course. There are also useful, in-depth discussions in the forum. Thank you!

por Lien C

4 de Abr de 2019

Great practical insights of how to start a ML project, how to improve/optimize the system, how to identify and troubleshoot common problems in deep learning. The course provides comprehensive high level guidelines for anyone who uses machine learning, even without having any programming experience!

por Dariusz J

19 de Jul de 2019

The course has practical content. When took in the Deep Learning Specialization I noticed that some parts of the material were already known from previous courses. Indeed, in previuos courses the repeated aspectes are presented from a different angle, but probably there is an area for limiting it.

por Jialin Y

21 de Abr de 2018

It's like understanding deep learning: a team leader's perspective. Andrew may be the first instructor to give this kind of course. Based on his experience in building practical and large scale machine learning system in Google and Baidu, the course content is highly inspiring and worth listening.

por Ged R

3 de Out de 2017

As an Ops person by nature, i like to see methodology and structure along with systematic approaches to results - be they solutions or problem solving. This course adds to that area, by providing best practices and ideas, it forms the basis from which these challenges can be addressed. Very good.

por Akshay M P

25 de Set de 2020

THE must have course for every machine learning enthusiast!! The course is very enjoyable with invaluable insights and expertise from a well-rounded deep learning practitioner. It greatly helps to clear the machine learning workflow and best practices to quickly develop, iterate and ship a model.

por Mihai L

28 de Jan de 2018

This course had no programming assignments. Yet I found it amazing. It truly gives you insight into how to engineer your projects to account for real world conditions.

Liked the flight simulator analogy to this course. Accelerated learning is really the great benefit of following Andrew's advice.

por Gabriel L

25 de Ago de 2017

I've done a Master degree in IA and the things covered in this course have never been addressed by any of my professors. Now I've been working in a Machine Learning team for the past two years now, and I believed these lessons would have been of great value, and would have saved me a lot of time!

por Ruben G

7 de Ago de 2020

This a great course on Deep Learning, the contents are so full of interesting information, actually, this course could also be called "Everything you wanted to know about Deep Learning (but were afraid to ask)".

As always, Andrew delivers a great course, whose content is ready to put in practice.

por Julio E H E

16 de Jun de 2019

This course is very helpful to learn best-practices and problem-solving strategies that can help improve our deep learning algorithms. While I think the ultimate way of learning is through practice, here you can at least get a list of things to try in the future as you work on these algorithms.

por ANSHUMAN S

26 de Mai de 2019

Although this was a bit hard for me to understand but still through the quizzes i got an insight so as to where will these advice be applicable and where i can use what i studied.

I am thankful to the teachers and a especial thanks to Coursera for giving me the opportunity to avail this course.

por Virgilio E

17 de Dez de 2017

I think this part of the specialization is a great value key, and makes the difference with other courses, self learning books, etc. The contents of this individual course helps a lot into understand and improve knowledge studied in previous and next courses. I definitely recommend this course.

por katherine t

14 de Dez de 2020

A very unique course. It's like having a casual conversation at Andrew Ng's office, where random bits and pieces of industry knowledge comes up. Though the field is rapidly evolving and some of the "best practices" keep changing, the underlying philosophies still make this a worthwhile course.