Andrew Ng
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 …
Instructor Details
Courses : 4
Specification: Structuring Machine Learning Projects
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105 reviews for Structuring Machine Learning Projects
Add a review
| Price | Free |
|---|---|
| Provider | |
| Duration | 7 hours |
| Year | 2017 |
| Level | Beginner |
| Language | Chinese(Simplified) ... |
| Certificate | Yes |
| Quizzes | Yes |
FREE
Shahbaz Q –
Awesome and felt closer to the actual machine learaning job
Anshuman K –
Very well explained. The flight simulators were a particularly good idea!
Hugo T M K –
This course is exceptional since we can learn a lot with Andrew Yang’s great experience with Machine Learning Projects. It’d also like to suggest to add new classes about powerful and newer techniques, such as feature visualization.
Michael D –
Excellent and filled in a lot of holes just like the other courses
Angam P –
Great Course giving great real life experience.
mitra_00 –
Learned about how we should manage our DL project and what to priotize first, these are something one learns after he has gone through such problem, so it was nice to learn about it beforehand from the expert
Ayush K –
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!!!
Rooholla K –
I thank dear Andrew for preparing a course that teaches us the things that take an ML practitioner years to master on his/her own.
JOYJIT C –
Excellent explanations, well created labs. Thanks a ton to Prof Ng.
mitra 00 –
Learned about how we should manage our DL project and what to priotize first, these are something one learns after he has gone through such problem, so it was nice to learn about it beforehand from the expert
Jacob S –
Even after working in the field for many years, I find that I learn something new in every video. Andrew really captures well what is important from both practical and theoretical perspectives and is a master at explaining concepts in a simple, but not dumbed down manner.
Jaffer K –
Interesting learning different metrics which can be used to identify problems in Machine Learning Projects in order to avoid wasting large amounts of time and energy on wrong metrics.
Habibur M –
Thank you for all teachers. You give me a new perspective of learning.
Lois A L –
This is phenomenal. I was working as an undergraduate student researcher in two different projects right now and what was discussed here really gave me the eyes that I really needed.
Hector L –
This was a short course, but it was packed with first hand knowledge of the industry. I enjoyed the quizzes/simulations!
Alexandre F –
useful insights into how to deal with a DL problem
Sujeet K –
Finally completed course 3. Learned a lot. Easy to understand.
Miguel A Z G –
Those case study were awesome! totally recommended!
Moses T –
Excellent paradigmn foundation to approach advanced system of deep learning
Han L –
The insights provided by Prof Ng is second to none.
Madagama G B S –
This course helped me to systematically analyze errors in deep learning implementations. The machine learning flight simulator is a great way quickly learn how to address issues you would face in making practical machine learning problems.
Gayatri G L –
Clear Concepts where to use which technique to get better performance.
Shalitha P –
extremely good. gives a good idea about how to attend our own machine learning problem
Payam M –
Well organized and lots of great practical information. Highly recommended.
heykel –
very helpful to build an intuition for DL strategies…
Dean M –
Nice course in addition to the other course in the specialization. Could be longer and deeper but still it’s nice to get another insights.
Lewis C –
Good course. Very interesting! Having done the course, most of the ideas seem fairly obvious. However, the chances of me coming up with them on my own are almost 0. Therefore I think the training has been successful.
Ashwin A R –
It was an amazing course that helped me better understand the practical organization and application of AI/ML projects
Carlos d l H P –
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.
Oliver D J R R –
Nice
Charles D –
Good for the stage just after learning the basics of deep learning. Good for orienting myself confidently in a direction to solve problems with DL.
Manish C –
Nice to learn skills about project handling in machine learning.
Marco M –
Very good insights about how to optimize and debug real world deep learning applications.
Sreenivas N M –
Really well structured concise course. Knowing what to do when in a machine learning project is really important and this course teaches that.
Pranav B –
Good Course for Beginners need more programming assignments
Zhuo C –
There is no project in this course, but I’ve learned some strategy, which is really helpful, too.
Luis d l O –
Very practical advice from Prof. Ng. Many of these are the kind of things that nobody tells you about and you have to discover yourself
Jairo J P H –
El curso es muy bueno, particularmente estoy muy agradecido con COURSERA, por darme la oportunidad de hacer los cinco cursos de la Especializacion en Deep Learning con ayuda economica y permitirme tener acceso a este tipo de capacitacion y certificacion. Muchas Gracias&! The course is very good, particularly I am very grateful to COURSERA, for giving me the opportunity to do the five courses of the Deep Learning Specialization with financial aid and allowing me to have access to this type of training and certification. Thank you very much!
Linda T –
I learned a lot of basic knowledge in ML; the teacher’s beautiful voice and his diligent sharing information to our student, make me so appreciated
Adrian S –
This course really provided me lots of insights how to apply machine learning to real world problems. Consider this to be even more important than in depth details.
amit k a –
Great !
Alexandr G –
Great systematization of knowledge, even for people with extensive experience!
Tanay G –
I was sceptical at first, it seemed that the course would just teach a lot of theory which won’t be relevant. I am happy to say that I was wrong, the course gave me a better understanding of how to take various decisions for a particular machine learning problem. I liked this course a lot.
Piyush K B –
Awesome techniques taught by Andrew Ng. Found myself lucky to find such a tutor.
Fabrice H T –
Great Courses!!!
Vinay K –
Info about the approach in applying DL/ML concepts to various scenarios were explained.
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!
Thanh T P N –
Very useful course. It helps me to make more precising decision and bring up many ideas about developing a neural nets
Mengting J –
Very good!
Marshall –
Very interesting case study based quizzes that made me think about the new material.
zheng –
Great course! I learned a lot of best practice on how to structure a ML project, from metric defining to error analysis.
Victor –
Excellent course on learning how to plan for different stage of a Deep Learning project and common potential issues people would encounter.
Norelys R –
Although you won’t write code. This course teaches multiple and necessary techniques very useful in a real problem. Very interesting
Prashant A –
Very informative course
Yuanxin L –
Very good!
AKSHAY K C –
Truly a great course by the instructor and his team. These real life problems are certainly not taught anywhere. Kudos to instructor and team for delivering such a good course.
Reza S –
Thanks Andrew for this course! However, it is obvious that less care was taken for the preparation of this course compared to previous courses (more typos, etc). Some of the sentences in the quiz were not clear at all and made it very confusing to choose from the options. A little programming assignment at least would be nice to reinforce our learning of the materials.
Mukkesh G –
By far the most important course of this specialization. Really wonderful insights.
Preeta –
great content and fantastic way to practically learn various aspects of a real time ML project. Appreciate the great instructor & lectures!!!
Roch A –
inspirant
Soham B –
Perfect course for your if you are looking for strategies to improve your Machine/Deep Learning model
Luis M R –
Very helpful to understand different options to progress improving your solution of deep learning.
Alejandro J C O –
The course was really great, but a little part of the content was repeated from previous courses of the specialization. Also there should be more quizzes or exercises to master the large amount of practical advices for managing machine learning projects.
Pranjal J –
Nice and well taught course about the techniques of improving deep learning models.
SHAO G –
very helpful to me, one DNN noob
J. F. R –
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.
diego s –
I miss notebooks for practice, besides questionnaires
Kan X –
I like this specialization in general. However, this third one has too many overlapping contents and some videos are not that useful. Just personal opinion.
Lucas N A –
I liked this course. It has really good tips and the quiz is challenging enough :).
Aditya M –
It’s a quality course with needful things to empower the beginners for making strategic choices while working on their ML project. Very insightful.
oman.ispace –
Rather less coding in this course. However, if you are about to embark in ML project, the advice you will get here is valuable.
Evan D –
Super helpful practical knowledge for ML Projects
Simone I –
Very well done
ARCHIT J –
Anyone can train, but how to improve in a systematic way, can be learned from this courses.
Shawn B –
good
Mohammad H Z h –
From this course, I have learned unique techniques to handle my own future projects. Thanks to Andrew and Coursera.
Jeffrey D –
Maybe a bit too easy?
Mohamed A B –
perhaps adding code on how to synthesize images and code examples on dealing with avoidable bias and variance
Felippe T A –
For me, this is the best course between the first three courses of this specialization. The content here is the fruit of the experience of the professor and can not be found easily on the internet neither in books. Congratulations DeepLearning.ai for this amazing course.
Nilesh S –
Good Course
qiaojiaxin –
Very nice ,if there will be more programming exercises,it will be perfect.
Thomas A –
Interesting but not very straight to the point
Noam B –
very unique course and will help in future
Berkay G –
very helpful and informative, thanks.
Rohit G –
The pacing was a little off, and perhaps some code would help. Otherwise, pretty good.
Soumya D –
An essential part is covered in this specialization,i.e Error Analysis and Model’s different kinds of learning techniques with very practical case studies are so much helpful.
Sanjay K G –
Too good
Sajal C –
Truly helpful if you want to take a systematic approach for your project.
CIndyLYP –
useful and easy to understanding
DI C –
Great course for strategic part in ML projects. The project based simulator is a good way for exercising the ideas learned in the lectures!
William –
Great course. Looking forward to putting this into practical application
Tanuj D –
This was by far one of the most challenging courses in the deep learning specialization as it covered a lot of practical ml implementation. I personally think that the ideas and the strategies discussed in the course will be highly useful while implementing real life models. The assignments are very well designed and created a real life scenario environment
Shyam P –
The course was full of knowledge and resources such as the quizz. Andrew Ng Sir teaching and examples were awesome.
Karthik R –
A very unique course and it thoroughly stands out in its attempt to give an insight into real world application of machine learning. Amazing work guys!
Ronald C –
One of the very few courses that teaches how to improve the performance of machine learning models in practice.
Ali K –
In this course, the instructor from his experience gained through several machine learning and deep learning projects explains how to prioritize tasks in a big machine learning projects. This course does not introduce the reader to CNN or RNN but rather makes the user aware of some ML/DL tips to make the most efficient use of time and resources. Some of the most important questions addressed in this course are: 1) Why a single evaluation metric is important and what are some of the widely used metrics? 2) What is human level performance and is it a good estimate of Bayes error? 3) What is Orthogonalization in the context of ML tasks and why is it important? 4) How to measure avoidable bias, variance error, data mismatch etc? 5) How to address data mismatch error? What is transfer learning and how is it different from multi tasking 6) Whether one should opt for traditional or end to end deep learning approach?
Sathvik B –
useful info not found
Bahromov J –
typos in quiz
KISHOR –
this course was a little boring, but it covers all the necessary concepts about the error analysis and strategies to be followed in machine learning
Mohammad J P –
Great course!
Danielli I –
This is a great course with excellent contents and guidelines ! Point for improvement: Please add a programming assignment in python and the questions appearing during the lecture….
matheus g –
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.
Bany I –
Scenarios really force you to think through the different outcomes of each decision.
Abhishek P –
Nice Learning Experience
Swapnil T –
What can be better than this, a highly qualified and passionate individual explaining what he has observed and learnt from the mistakes of other professionals , those who themselves are one of the smartest brains so that we don’t make mistakes or waste our time realizing that we were hitting something wrong.