Learner Reviews & Feedback for Structuring Machine Learning Projects by DeepLearning.AI

Amazing Courses for Deep Learning Specialization, help in making you good with basics needed, and apply the concepts in real-world applications.

Thanks to Andrew Ng for the way he explains every topic so deeply and easily understandable manner.

It's an amazing course for people without ML experience overall. But if you have some experience (deep learning or not), you'll find a lot of basic tricks that you probably already used or figured out intuitively as you worked on some projects.

I learnt things that I did not found in books. Very practical and with a lot of realistic examples. I recommend this course to experienced Deep Learning developers/engineers who want to improve their skills on real projects with Deep Learning.

Knowing just theories won't guarantee one's success in a specific field like deep learning, theory, and experience are both what it takes to be so. This course conveyed not only theory but also many years of great experience from professor Ng.

This is one of the best courses in data science I have ever taken. There are a lot of options out there for taking data science courses, but this one is key to getting organized and know how to proceed with a project what to do at each step.

One of the most helpful 8 to 10 hours of instruction I have ever received in my life. Thanks a lot Andrew, and the entire team for putting this together. I will recommend this course to all my friends and colleagues working in the AI/ML space.

Perhaps the most important course out of the 5 courses, Professor Ng explains really important concepts often overlooked by a lot of machine learning/ deep learning tutorials. This course will really make your good algorithms great.

Cheers! :)

the question on synthetic fog, I would love to know if someone answered to this one right from the first time. It is a designed trap to see if we are just listening to the class and applying or if we really think and work hard on each question

Awesome course. Hats off to Andrew for how this course is organized and presented. The simulator exercises are very good too. I'm yearning for more such exercises. It feels like I gained real-world experience without working at an AI company.

The case study approach to teach students is really awesome. This way the concepts that you learn in the course are so easily set up in your brain, it's just awesome. Thank you Andrew for your dedicated efforts to help us learn Deep Learning.

This was an interesting course which gave me an opportunity to really think about different machine learning problems and how to go about addressing these. I think the notes I took are going to prove invaluable as I go along in my AI journey.

this is the greatest course I have ever seen in machine learning and deep learning. it gives students the best practice for applying machine learning in real projects and gain a lot of experience from one of the best machine learning experts.

Practical tips distilled from years of hands-on experience delivered to be understood easily and intuitively. Will save a lot of time on getting started or getting accelerated on projects esp. for those with beginner or intermediate skills.

Andrew again delivered a great course! He talked about his real-world experience in creating and delivering ML projects in this course that is super helpful. I never see any other instructors who cover this important aspect of ML projects.

Great course! It would also be very helpful to have the final slides of handwritten notes by Andrew as .pdf files to take a look at the content later without having to watch the videos. This course is extremely helpful. Thanks to all of you!

this course is very wonderful. I was really impressed with the amount of information acquired and that will be very useful when developing my ML applications. thank you very much for these many years of experience transmitted in this course.

Amazing course taught by Professor Andrew Ng. I love the piratical insights that I gained through this course. I believe this course is a must have for someone who wishes to tackle deep learning problems in a systematic and organized manner.

Very instructive course, full of practical and actionable advice to focus on the right problems in an ML project. The "flight simulators" are concrete examples of decisions one has to make in an ML project and it is good to practice with it.

It does a great job of providing guidance on how you would plan a deep learning project. Transfer learning in particular is a very intriguing approach to leveraging previous work to speed up training a new neural network for your new task.

Material is excellent, Andrew is a brilliant teacher. Learned a lot. (Minor complaint: week 2's questions are formulated in a confusing way, making it hard to answer correctly even if you've understood the material of the course perfectly.)

@Andrew Ng: Your statement "And I think that phonemes are an artifact created by human linguists. I actually think that phonemes are a fantasy of linguists." in: Whether to use end-to-end deep learning" Week 2, ROCKS !!!! GREAT and agree...

This is an excellent course for anyone in Deep Learning, Data Science, or Machine Learning. It is a little on the short side, but packed with good ideas about how to structure your projects when considering various differing data scenarios.

Exceptionally good course.It gives brief idea of how and what strategies should be used while approaching any problem or building the system .Also gives idea about how to improve the efficiency of already build system and upto what extend.

Excellent course that discusses a lot of details and nuances about machine learning and deep learning that are drawn from Andrew's own experience as a prominent researcher and pioneer in the field . I feel I gained a lot from this course .

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.