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Structuring Machine Learning Projects

Deep Learning,

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.

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Rating 4.6 based on 3,655 ratings
Length 3 weeks
Effort At the rate of 5 hours a week, it typically takes 4 weeks to complete this course
Starts Jun 26 (49 weeks ago)
Cost $49
From via Coursera
Instructors Andrew Ng, Teaching Assistant - Kian Katanforoosh, Teaching Assistant - Younes Bensouda Mourri, Head Teaching Assistant - Kian Katanforoosh, Kian Katanforoosh, Younes Bensouda Mourri
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming
Tags Data Science Data Analysis Machine Learning

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What people are saying

machine learning projects

Very good overview of the challenges in machine learning projects and pointers to overcoming them.

An Excellent way to manage your machine learning projects .Highly recommended if you in a situation where you stuck what to do next to increase your accuracy Really great Course, learn a lot.

There are not much available online resources to learn about how to structure and manage a Machine Learning projects.

It helps to structure machine learning projects to shorten the time and effort and to prioritize the different alternatives.

wow There are a lot of basics that are necessary to learn Very practical course content along with the very effective machine learning simulator problems I think that this course will give me help about solve problems when i work with machine learning projects.

The topics discussed in this class are very closely associated with the title `Struturing Machine Learning Projects`.

Short and efficient lectures for preparing for structuring machine learning projects in the real world.

Many concepts regarding approach to machine learning projects were useful.

Very good and useful experience on building machine learning projects.

Good to learn more about how to tailor your machine learning projects.

This course has some valuable insights on how to organize and systematically move forward in machine learning projects.

Lots of principles and skills about how to organize machine learning projects and diagnose problems.

I learned the best real world developement strategy on machine learning projects.

Practical knowledge that tells people what to do next when problem occurs in AI project "Structuring Machine Learning Projects" provide so many good practices in how to correctly implement Deep Learning Models, troubleshoot them and make them better, the tips and recommendations are excellent, highly recommended to anyone interested in deep learning this is a fantastic Course, thanks to everyone that make this Course possible.

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neural network

This course follows Neural Networks and Deep Learning and Improving Deep Neural Networks.

Valuable insights into how to structure ai projects with the respect to data, new data, buggy data, synthesized data, mismatched data, and much more such as error analysis and how to use pretrained neural network.

I took this course soon after completing the Machine Learning course, before starting the Neural Network and Deep Learning.

This course gave a chance to understand organize the neural networks and machine learning.

Thanks Andrew 希望本课能增加一些编程作业 Lots of good advises and guidance for analyzing the performance of neural network effective and efficiently.

Gives a sense about improving the performance of Deep Neural Networks, with error/bias/variance/data mismatch analysis.

Very good insights on how to go ahead in stuck up projects Having talked to someone who is actively working on Neural Network models, some of the insights I learned from the course looked to be helpful to them as well when we talked.

求中文字幕 Amazing high level overview of neural network alternate implementation options.

It's very easy now-a-days to create Neural Networks and get a grasp of how they work due to high-level frameworks (keras, scikit, tflearn, etc) and abundance of literature and videos, respectively.

I have in the past build some complex Neural Networks, but would hit road blocks that would ruin productivity for I didn't know how to approach problems correctly, and didn't know what knobs to turn to improve performance of my program.

This course contained a lot of great practical tips for implementing Artificial Neural Networks and structuring your own machine learning and learning projects!

It has been an really awesome experience learning about neural networks from you.

A clear explanation of a difficult subject with an emphasis on being able to create and to understand your own neural networks.- Plus in this module how to allocate your resources so you can achieve a successful project.

Not much to learn in this course, basic recommendations can be condensed in one or two lectures Nice presentation on how to start and drive your deep neural network construction.

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case studies

Excellent I really liked the case studies as it helped understand the real world scenarios Practical approach examples are really good for thought formulation.

I felt the case studies were amazing.

The idea of the case studies was great!

The case studies in the quiz are extremely helpful as some concepts can be a bit confusing and they help clarify the doubts you might have in the subtleties between the different situations you may find.

I've also missed having a practical home work, the case studies were fine, but I find that practical applications help me remember things better.

The two case studies are challenging but equally interesting and helpful.

Case studies are incredibly useful and give a global understanding of the different aspects in a ML project.

However, understanding the importance of strategy, either additional scenario quiz (the simulation type quiz is good) or a programming assignment would reinforce the understanding (given short duration of the course) Great Course and great quizzes The case studies are very helpful.

The tips and case studies do not always work in real application.

I liked the case studies.

Loved the case studies.

This time, i've missed some programming assignments, although the case studies was very instructive of the practice, some programming experiments with transfer learning will be great.

(3) While I think the approach of having some themed case studies for the test is neat, a lot of the answers left me thinking "well, the correct answer would also depend on X which isn't specified".

I would have liked more hands o examples good Could have more case studies and above all.

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neural networks

I think 2 weeks on this course will put you ahead by 2 years in your path of building neural networks for solving real world problems.

Improving performance of your neural networks in a structured way - something which is not easy to find as a resource on internet.

Really excited to see what challenges you've got in store for us in the upcoming Convolutional and Recurrent Neural Networks courses.

I feel much more able to construct my own neural networks, diagnose and solve issues with them after following this course.

This course offers some good advice when it comes to (much needed) practical considerations when training neural networks, and to a reasonable extent machine learning algorithms in general.

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.

New interesting views on practical issues in artificial neural networks projects Very insightful and practical strategies for machine learning projects After seven days learning, I finally finished the three course of this specilization.

Great content on how to tune and adjust your neural networks for a proper deep learning solution Learned a lot about dealing with datasets where training data and test data might not have the same distribution.

You can learn a lot about improving neural networks and troubleshooting.

Despite all the samples are using neural networks, the methodology can be applied to improve other machine learning projects.

very informative course Enjoyed thoroughly and full of practical guidelines... very useful tips for real world development I thought this course was very helpful in analyzing neural networks.

This is an amazing course for people that are just starting with neural networks and for advanced learners too.

It was an amazing course that helped me better understand the practical organization and application of AI/ML projects Actually adds some insights I hadn't learned (or at least I was guessing but it's always nice to have a double check) after 4 years as a data scientist.Also, some of those insights are very specific to neural networks projects, so doesn't matter how many years have you been working if you've never made deep learning projects this will help you nevertheless.

Fabulous material This course gives a very intuitive understanding for analysing performance of neural networks and strategies to go about improving them.

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highly recommended

Highly recommended :) Coding exercises would be great in this course.

Highly recommended.

nice simulation of the problems in the quizes Highly recommended to advance ones' career into deep learning Great course !!

Highly recommended!

Highly recommended for professionals who would want to explore deep learning /AI Excellent practical insights for real-world scenarios Great insights and solutions to solve real world deep learning issues very useful and clear.

Highly recommended Interesting lectures.

It is highly recommended, and you can have a clear and broad-view of how to organize and manage a mature DL project Good, practical information to help tackle ML projects most effectively.

Highly recommended for deep learning project managers.

It is a great course, highly recommended for those who wants to work in the AI This course could have just been an extra week or two of course 2.

Highly recommended as it helps one think how to improve their ML models.

This course is very helpful to fine tune our machine learning and our deep learning projects and probably more input to the different types of transfer learning examples could have been much more helpful Highly recommended.

Again overall - highly recommended Could have been more in depth or could have been added to another course as one extra week Good Course Topics are a bit vague, which is fine as the content is interesting and useful nonetheless, but perhaps exposition is too lengthy relative to the amount of content.

Highly recommended Great hands on experience with deep learning.

Excellent Course that gave me a lot of insight and much of practical advice.Highly Recommended.

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it would be better

it would be better to do it in three weeks awesome course Great tips!Minor issue: often the request for feedback for a lecture came right at the beginning of the lecture, covering big portion of the video ('was this video helpful'!

The course showed the experiences while dealing with machine learning projects but could have been better if the experience would have been shared through practical exercises rather than objective case study.It would be better if there were programming exercise as well.

It would be better if we could try coding and experimenting these ideas.

It would be better if you include programming exercises.

It would be better with programming assignments.

It would be better if the background would be transparent.

Compare to other courses of the specialization, this has lower quality of video lectures, often repeats things from previous courses and I think it would be better to separate whole course as a separate week of a previous one.

It would be better to have some hands on assignment or quizzes.

Great Course Many useful trip and trick for ML project If there is some coding practice, it would be better It is having a great information from starting a new project to training the model.

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previous two courses

very useful for machine learning engineers Dr. Ng set the bar very high in the previous two courses of the specialization.

这门课从很多角度为我们介绍了实际情况中训练机器学习模型可能遇到的问题和解决方法,是一个很有实战意义的课程,收益颇丰! Very useful to take a break and think about project strategy This is a 2-week follow-up on the previous two courses in this specialization.While it's a decent course that goes over a few interesting topics, I have a hard time giving it more than three stars.

Excellent course material would have been icing on the top if there were some course exercises like previous two courses.

I feel that this course should have been the last course in the series instead of the 3rd course This course had a much less ambitious scope than the previous two courses and I think that the programming assignments are very important to help me learn properly.

Compared with the previous two courses in this special, this course is more practical and useful when we are actually trying to solve real-world problems.

Useful introduction to meta-level principles of machine learning process management, but not quite as groundbreaking or well-instructed as the previous two courses Good practical advice.

Easier than the previous two courses.

Provides good intuition on how to structure deep learning models Much easier compared to the previous two courses.Teaching more practice experience and advice rather than algorithm and coding.

I would highly recommend this course who have completed the previous two courses.

This course was smaller and a bit more theoretical than the previous two courses.

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no programming assignments

However, there's no programming assignments and lectures are lengthy.

Why is there no programming assignments?

This course had no programming assignments.

很好的DL课程,内容非常使用。 Good insights, but not much material and no programming assignments.

The second issue I find with the course is that there are no programming assignments.

Unique insight, rarely available Excellent course for ML enthusiasts Although I see other learners saying that this is the worse of all the Deep Learning specialization courses because there are no programming assignments, I believe it was a very useful course full of practical knowledge for properly structuring ML projects.

Suggestions: Unlike other courses, no programming assignments here .. may be some programming assignments + Quiz in a case study format would have been more helpful.

Practical ways to manage a project to reduce errors in a systematic way Notwithstanding the great video lectures this course's assignments were poorly composed:Firstly, there are no programming assignments!

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looking forward

Looking forward to course 4 in this series.

I am now looking forward to the next 2 courses.

Looking forward to next course.

I am looking forward for the next 2 courses.

Very much looking forward to RNN and DNN (modules 4 and 5).

Looking forward for the next course!!

Looking forward to the last two.

High quality lesson, looking forward to lesson 4 and 5.

Looking forward to Course 4 and 5.

I am looking forward to the next course.

This was the most useful course from the first three courses in this specialty; looking forward what will be next could be a little slow at time as its mostly video content notes, but still good Would have liked some coding Extremely valuable practical information regarding how to structure ML projects.

I've completed the first three course of the specialization looking forward to start fourth one.

I'm looking forward to have a next level course on top of this track.

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flight simulators

Found the "flight simulators" were not s Good insight to managing larger learning projects i liked these 2 flight simulators course, where i get to be in a virtual scenario, to take the right decision, to save the team and the project.

Case study flight simulators are good, but poorly introduced.

Useful A lot of practical insights - the flight simulators were helpful.

On the other hand, the two "ML flight simulators" are really interesting and answering them is not obvious.

The flight simulators' results were not consistent with the advice provided in the lectures.

没有编程练习。 learning so much Nice and challenging Quizzes with flight simulators .

The "flight simulators" are concrete examples of decisions one has to make in an ML project and it is good to practice with it.

I loved the idea with the case studies to flight simulators.

The flight simulators were a particularly good idea!

Flight Simulators look very efficient Useful for those who are thinking of ways to debug their AI projects and also learning methods to deal with errors, biases and treating datasets.

The flight simulators are really interesting and generate interest in the field.

Sometimes a little to repetitive, but great insights and these flight simulators are NIIIICE.

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even though

This course did not contain programming assignments, only quizzes, and was thus considerably less useful, even though the knowledge was important.

Even though it only mentions deep learning, the overall methodology can be applied to any machine learning work.

even though there are great tips and advices, it does not justify an entire course and they can be mentioned in 3 videos so a lot of the videos were repetitive.

This was a slightly more theoretical course than the first 3 in the Deep Learning specialization and, even thought I enjoyed it, I think the info would stick better if there would have been a programming assignment too (or some other type fo practical application).

Even though it feels very high level at first, it actually gives us clear directions when handling deep learning problems in reality.

Moreover, in order for someone to deeply comprehend these concepts such that he/she is able to apply them in a Machine Learning project, he/she should work on a project on his own where he/she will meet these concepts and will have to search in order to solve them.Last, personally, even though I am quite satisfied from the courses, I would expect that one more course is added to Coursera which is going to require to build a Deep Learning project!

But the whole specialization is great Even though I'm quite experienced with training models, I find this course is very useful and can give me valuable directions.

Even though some of the content is useful, I feel like this course should be merged with the second one.

Even though I have the experience of working on deeplearning there are some points I can take from this course Awesome as always good I love it.

One of the best courses I have ever gone through, the lessons were short and to the point thus allowing me to absorb the concepts even though they were bit outside my experience.

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anywhere else

I have not seen these materials anywhere else.

Andrew shared a lot of first hand experience which I can hardly find in such a systematic way anywhere else.

Very good advice that is hard to find anywhere else.

Innovative contents full with good advises and suggestions in the matter You won't find the materials of this course anywhere else.

real life practical project experience which is probably hard to find anywhere else First of its kind of course for systematically proceed a ML project.

Distills the key and practical aspects of machine learning projects Full of fantastic practical advice that I haven't found anywhere else about machine learning in the real world, when you're rarely given the ideal amount of data.

This course provides practical advice and recommendations for teams building real-world applications of Deep Learning -- advice garnered over many years of work by Professor Ng and others, and, as far as I know, not collected into a single source anywhere else.I have taken several of Professor Ng's courses.

You will not find this information anywhere else.

The things I learned in this course have not been taught anywhere else.

This course is unique in content and you cant find anything like it anywhere else.The amount of experience that Andrew conveys is enormous and practical tips that only can come from a real professional like Andrew.

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An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Research Scientist-Machine Learning $55k

Cloud Architect - Azure / Machine Learning $75k

Watson Machine Learning Engineer $81k

Machine Learning Software Developer $103k

Software Engineer (Machine Learning) $116k

Applied Scientist, Machine Learning $130k

Autonomy and Machine Learning Solutions Architect $131k

Applied Scientist - Machine Learning -... $136k


Machine Learning Engineer 2 $161k

Machine Learning Scientist Manager $170k

Machine Learning Scientist, Personalization $213k

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Rating 4.6 based on 3,655 ratings
Length 3 weeks
Effort At the rate of 5 hours a week, it typically takes 4 weeks to complete this course
Starts Jun 26 (49 weeks ago)
Cost $49
From via Coursera
Instructors Andrew Ng, Teaching Assistant - Kian Katanforoosh, Teaching Assistant - Younes Bensouda Mourri, Head Teaching Assistant - Kian Katanforoosh, Kian Katanforoosh, Younes Bensouda Mourri
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming
Tags Data Science Data Analysis Machine Learning

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