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Structuring Machine Learning Projects に戻る による Structuring Machine Learning Projects の受講者のレビューおよびフィードバック



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....




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.



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.


Structuring Machine Learning Projects: 101 - 125 / 5,517 レビュー

by Kayne A D


More of a general comment for the specialization but I love the Andrew and the teaching team have set up the content delivery. Simple, clear and well-paced delivery with consistent use of well-considered examples. On top of that, the summaries are great representations of key concepts. I am greatly appreciating the entire specialization and seeing the bigger picture in terms of why it is structured as such. Thank you!

by Andrei K


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.

by Robert K


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.

by Shringar K


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.

by George Z


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.

by Johan B


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!

by Sebastian E G


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.

by Johannes B


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.

by Kyle H


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.

by James J


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.

by Rohit K


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.

by Christian C


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.

by Claude C


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.

by Leah P


A really helpful course on how to take decisions when approaching a problem, how to make error analysis and what conclusion to draw from that. What to give importance to have a more robust model. This courses learning materials are what will be making 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.

by Suchith S


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.

by Kishore K T


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.

by Manish H


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.

by dhirendra k


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.

by Glenn B


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.

by wirelessmusic


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.

by Lionkhan21


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.

by Ernie H S


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.

by Lin Z


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.

by Michael M


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!!!!

by Parab N S


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.