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Отзывы учащихся о курсе Structuring Machine Learning Projects от партнера deeplearning.ai

4.8
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Оценки: 39,151
Рецензии: 4,298

О курсе

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....

Лучшие рецензии

MG

Mar 31, 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 23, 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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76–100 из 4,258 отзывов о курсе Structuring Machine Learning Projects

автор: Andrei K

Apr 21, 2020

I find this particular course in the whole specialization especially useful so far. Andrew teaches great strategy that helps think and act on deep learning projects in a more systematical way, and does so with crystal clean examples. Quizzes in this course, similar to flight simulator, are great at ensuring you can apply the principles you have just learnt and see where your understanding is a little bit vague.

автор: Robert K

Nov 19, 2017

Fantastic lectures combined with case-studies for real world applications. In this course you don't program, but don't underestimate the ability to abstract out and systematically assess your thinking. This could speed up your project development and save you tone of time. Any potential employers would also be happy that you know some practical aspects of implementing a deep neural network for a particular use.

автор: Shringar K

Jul 28, 2019

Honestly speaking, this is the best course in the whole deep learning specialization. This course is the one which tells us what to do as a Deep learning engineer in real world scenario.

People can do the coding and everything, but without proper directions the product might fail.

Andrew Sir has given his expertise in a very neat and compact way, good enough for starting our own research or whatever we want to.

автор: George Z

Aug 04, 2019

Amazing 3rd course, I learned so much related to error analysis, bias, variance, data mismatch, data synthesis as well as transfer learning, multi-task learning, end to end deep learning and more. I really loved both case studies in the end of each week. The interviews especially with Andrej Karpathy was my favorite :) Excellent best practices and strategies that you don't learn from any other course or book.

автор: Johan B

Sep 22, 2017

This course in the specialization is less about how to build a model but gives you a structured way of how to approach a deeplearning project. It shows how much some manual (and maybe boring) counting can speed up your project and that starting with a simple model and iterating on that often outperformes very detailed thinking about your project at forehand.

The practical tips Andrew shares are very valuable!

автор: Sebastian E G

Aug 18, 2017

Liked this way more than I thought I would. Machine learning project management is vital in a professional setting (I would assume), and I often leave it as an afterthought. It's not just building the fanciest model, it's about how to iterate from an okay model to your best model in an efficient manner. This course teaches you what to look for with your results and pinpoints what areas to focus on to improve.

автор: Johannes A B

Feb 27, 2018

Very nice introduction to the aspects of a machine learning project that is not covered other places, but is very important. Most of it is very intuitive and comes as no surprise, but it is still very usefull to collect it into a single course. It is a good resource to have in case you are in doubt about how to structure your project, where to focus your energies and how to make progress in a systematic way.

автор: Kyle H

Jan 04, 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.

автор: Rohit K

Jul 06, 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.

автор: Claude C

Jun 08, 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.

автор: Kishore K T

Jul 10, 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.

автор: Manish H

Oct 30, 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.

автор: dhirendra k

Oct 12, 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.

автор: Glenn B

May 31, 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.

автор: Alex

Aug 25, 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.

автор: Asad K

Apr 15, 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.

автор: Lin Z

Mar 29, 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.

автор: Michael M

Oct 29, 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!!!!

автор: Parab N S

Aug 25, 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.

автор: Chanel C

Aug 19, 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 :)

автор: Zhiming C

May 02, 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!

автор: Eleanna S

Mar 18, 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?

автор: Jason T B

Aug 18, 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.

автор: Mangesh

Mar 18, 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 !

автор: Manh T D

Apr 01, 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.