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

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5,212 avaliações

Sobre o curso

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....

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.

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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101 — 125 de 5,159 Avaliações para o Structuring Machine Learning Projects

por Kyle H

4 de Jan de 2018

Great course by Andrew Ng, coming from his Machine Learning Course and seeking to work on Kaggle Competitions, this course provides all the knowledge necessary to approach any machine learning problem (with or without a team), and efficiently work towards a better algorithm. It's almost as if he gives you the tools necessary to optimize yourself which in turn allows you to efficiently optimize any algorithm.

por James J

30 de Set de 2020

This great course provided quite a few good ideas for addressing scenarios that may arise when constructing a deep learning model. In terms of improvements, would suggest updating the name of the course to 'Structuring Deep Learning Projects' rather than 'Structuring Machine Learning Projects,' as the model-specific techniques (and examples for other techniques) all mention types of Deep Learning models.

por Rohit K

6 de Jul de 2019

Hello Andrew, I am a big fan of you. Learning from your every course. Very unfortunate that I can do that remotely only.

One thing that I want to mention - Can we have lecture notes on coursera, just like the way used to in CS229 that we can read before coming to next lecture. I found that that was very useful in understanding when things get harder.

Thanks hope we can improve coursera in that matter.

por Christian C

2 de Jul de 2020

Based on personal experience with other courses on deep learning, much of the insights focus on what I'd call the "technical details" (e.g., maths and computations, using libraries, training strategies). I recommend this course for focusing on the "non-technical" aspects.

I find the lessons in this course helpful in streamlining the planning/strategizing process in the projects that I have been part of.

por Claude C

8 de Jun de 2019

Good engineering tips, tricks, bolts and nuts, very useful! Andrew Ng is more dedicated to engineering and best practices that are very important in the machine learning field which is not only theory (a lot less than some believe or pretend) but very empirical, with a lot of practices, try and and error, recipes. Don't be snob, maths are awesome but good engineering and best practices are crucial too.

por Suchith U S

8 de Ago de 2020

The practical aspects of what to consider and what not to worry about while getting started with DL projects were very helpful. It puts us in the right direction. The whole field seems to be a little overwhelming, but watching the experts breaking things down to more easy and understandable concepts with relevant examples is very much encouraging for a student like me to dig deeper and make progress.

por Kishore K T

10 de Jul de 2019

My Sincere "thank you" to Andrew Ng for teaching me ML and Structuring ML Projects. I find the content and presentation are on the highest level; which will definitely make the learner to think and workout in the correct direction when working in ML engineering and/or managing ML projects. I believe, in the coming times I'll learn more relevant topics from him to excel in my career.

Thank you again.

por Manish H

30 de Out de 2018

Excellent course - a unique short course where you'd get tons of insights from one of the top AI/ML experts Andrew Ng about how to curate data and structure your ML projects. Lot of practical and actionable tips.

Most useful course of the entire specialization to help you understand soul of the AI/ML development, you'll appreciate it even more if you have some experience of real-life AI/ML projects.

por dhirendra k

12 de Out de 2018

Thank you for providing this course. This course is something different, it takes you away from the technicality of the algorithms and makes you focus on a different but very important aspect of ML problems, i.e. error analysis. The professor is once again great in compiling all his practical real life experiences in teaching a subject which is not commonly found in other online training curriculum.

por Glenn B

31 de Mai de 2018

Great topics and discussions.

I get the dynamic aspect of writing the lecture notes in the videos, however the lecture notes should be "cleaned up" in the downloadable files (i.e., typos corrected and typed up). Additionally, the notes written in the video could be written and organized more clearly (e.g., uniform directional flow across the page/screen rather than randomly fit wherever on the page.

por wirelessmusic

25 de Ago de 2017

This course was helpful in basic undestanding of how to evaluate the data from deep learning models.

It took very diffrent aproaches like the precision and recall metric and even get faster evaluation with a f1 score. It was also helpful to get insight on diffrent types of errors which could show some direction how to optimize dev and test sets and why it is possible to pass beyond human performance.

por Leah P

6 de Jan de 2021

A really helpful course on how to take decisions when approaching a problem, how to make error analysis and what conclusion t draw from that. What to give importance to have a more robust model. This courses learning materials are what wil be makes a difference between someone who just trying out things randomly and someone who knows how to approach a problem to get better solution in a quick time.

por Lionkhan21

15 de Abr de 2020

Gained a lot of insight on how to structure machine learning projects, but I believe it would help for this course, and for the deep learning specialization to put lecture notes after each video in order to get a short and concise summary of all the relevant info we need to know, like the one in Andrew's into to ML course; however, Andrew is an insightful teacher, so I had to give this course a 5.

por Ernie H S

18 de Ago de 2020

There is a tendency to dive straight into applying ML to a problem and with the tools available today this is all too easy. It therefore becomes necessary to make sure that we are aware of how we can structure the process of machine learning. How we organise our skills/intuition/measures is what this course is about. Essential and in some ways as fundamental as the scientific process itself.

por Lin Z

29 de Mar de 2019

very good guidance on how to start a machine learning project, including many interesting discussions including how to choose the size of training/test/dev set, how to analyze the errors, how to deal with mismatched distributions of test/traning/dev set by adding a training_dev set and how to do end-to-end and multitask training. The contents are well exercised by two well defined case studies.

por Michael M

29 de Out de 2017

This is the best series of ML that I have taken so far on Coursera. Andrew Ng is a master at instructing others. I cannot say enough about this series, you would need to take the series to comprehend what I am trying to say. Somedays I watch and I am just amazed how Andrew takes a concept and turns it comprehensible at such a fundamental level. Great course it deserves more than 5 stars!!!!

por Parab N S

25 de Ago de 2019

Excellent Course on how to structure the Machine Learning projects so that the developers do not waste time following a random trial and error approach and rather take on an approach which is proved to work well in improving the accuracy of the model in spite of the changing requirements and data. I would like to thank Professor Andrew N.G. and his team for developing such a wonderful course.

por Baran A

14 de Dez de 2020

Another great course from deeplearning.ai. Again, Many thanks! to Andrew Ng and Coursera. Great lectures as usual. I have learned lots of new concepts and methods such as Dropout Regularization, RMSprop, Adam Optimization, Learning Decay, Batch Normalization, etc. I think the assignments were also helpful but not enough to absorb what I have learned. I'm looking forward to practicing more.

por Chanel C

19 de Ago de 2018

This course was very interesting. The examples are good chosen and the exams have great questions (they are summarising everything from the lessons). Great suggestions and also personal tip. I'm studying and I'm learning a little bit of these neuronal systems and machine translation which are based on language while your examples were more visual like the car case for example. Thank you :)

por Zhiming C

2 de Mai de 2020

This Part of study is a aimed to improve the skills during the Modelling and Calculation. In the realistic problems, people need time to get familiar with the process of how to build a sophisticated network. And the time to learn these experiences could be long. This course give us a lot of useful information and tricks. It saves our time and reduced the hardness for the work! It's great!

por Eleanna S

18 de Mar de 2018

I wish there was more such cases that I can learn from. I found this course very valuable. Thank you :)

I would be interested in participating in research. Do you think that Coursera could help with creating PhD degree/ applied research. I would like to improve the world by applying the knowledge I gained from this specialisation. Do you think Coursera could help with something like this?

por Jason T B

18 de Ago de 2018

This course should be mandatory for any machine learning practitioner, researcher, or student. Ng shares excellent insights and provides a clear structure for thinking about how to manage our most valuable resources in machine learning -- labeled data! The course discusses the concepts in a deep learning context but I would recommend even for those not working on deep learning problems.

por Mangesh

18 de Mar de 2018

I took this course soon after completing the Machine Learning course, before starting the Neural Network and Deep Learning. And found it extremely helpful, the simulator approach takenup in the course is absolutely spot-on and unique to this course (as compare to any knowledge source on internet).

Andrew NG has poured in his tacit knowledge and made it explicit in the best possible way !

por RUDRA P D

10 de Jun de 2020

This course gives insight to all the errors and their analysis, different approaches to deal with problems in machine learning and also working of different models such as Face recognition, Speech recognition and Automated driving models. Andrew sir explains all this concepts in a very learnable manner. I do recommend this course to those who are going to build their first ML model.

por Manh T D

31 de Mar de 2018

One of best courses I have taken on Coursera. There are not much available online resources to learn about how to structure and manage a Machine Learning projects. I would like to express my appreciation for all of the hard work and dedications professor Andrew Ng and his team spent on designing such a great course with understandable lectures as well as well-designed assignments.