Well

Good

good

Wow

Dd

Overall this is a good intro course to OCR using python. I was looking forward to this from the get-go of the specialization and it feels that i'm walking away with some understanding of the capabilities and the possibilities. that's great.

One comment regarding the presentation material - i thought the delivery was pretty dry. There was absolutely no difference between the oral and written material. this is in principle a good thing BUT not when there's info lacking. For instance we're at the PIL discussion and the comment is "I think that Image looks interesting"...well ok but why do you think that? what makes you think it's interesting. I've read the material first as per recommendation so I was hoping to get some insight as to why you chose one over any other...but nah. I don't know how difficult it would be for you to address this or you may choose to ignore it altogether but i've figured i should share since you're asking for feedback.

There are a couple of things i've found fairly annoying and it would seem to me that they can easily be addressed.

looking for image.zip for the last assignment -- I spent at least a couple of hours trying to find a copy of the image.zip file. I ended up downloading the py3.zip of 2 years ago or so. This is wasted time not time well spent. Why can this not be a link to the description of the assignment, meaning "go to the appropriate google share and download py3.zip", since the existing links to image.zip seem to be perpetually broken.

Kraken does not run on Windows -- this came as a surprise; not your fault but it's important enough to be posted up front so folks with windows machines, like me, can get their setup going. it takes time to setup a docker container with the python development environment etc to make it useful.

The instructor strives to show what working with 3rd party libraries is like -- it's a lot of searching around and figuring things out by looking at the documentation, looking at the code, and merely experimenting. It's instructive to see that finding this process somewhat chaotic is normal. I also appreciate that while doing so, the instructor teaches you how to use Jupyter Notebooks, which are pretty cool.

On the negative side, the final project was just too big to be done on the online instance, and it becomes a real nuisance to get the project done. The project is good, but doing it with fewer and smaller images might have changed significantly the frustrating bits. Overall, it was a good real-life project experience, with both the learning and the frustration included.

The teaching approach in this course is interesting. I found that working on an actual programming project is a great way to learn practical programming skills. I believe being able to search and make sense of other libraries/resources out there is a very valuable skill, which is what the projects in this course ask you to do. Therefore, a great course overall.

Two problems I have with the course are:

1) The Jupyter on Coursera. It was not very stable. Many times I had to restart the notebook.

2) The online submission method for the final project needs to be improved. Currently, there is no option to upload the finished assignment (in .pdf or .html format). I had to upload my file to google drive and share that link.

It is a good course since it tries to teach you how to approach and learn a new external module, by looking into documentation, etc. Also, it is cool to work with Jupyter Notebooks, it makes it really cool and presentable.

What's wrong? Submission system for week 3 project is HORRIBLE. Can't even copy the source code from the peer.

On the other hand, the scope of the course is really focused on Data Science applications, so if you were intended to master the art of programming, this won't be that helpful at all.

This course helped me improved my abilities, with a real world project at the end. I give it three stars because the last course of the specialization is poorly harmonized with the previous four: different teaching style, different platform, different course structure, different difficulty level, different teacher. Most students feel the gap and complain about it in the discussion forum. It is sort of unexpected. I am proud of my progress though, and glad I finished all five courses. Coding is the future.

This course is much more difficult than the previous courses in this specialization, which is exactly what I was looking for. Being able to research libraries, translate & externalize thoughts into code, and trouble shoot and fix bugs in a self-sufficient manner are all on the critical path to becoming a qualified programmer. You will likely invest a lot of hours in to the final project but your will learn and grow. I am giving this course one star less for the crappy Coursera code uploading system.

Compared with the four previous courses (part of Python 3 Specialization by U Michigan), this course is much more difficult, require ones to be proactive and deliberately learning. Unlike those four previous courses, the lecture videos never show the lecturer's face - instead, lectures are written in the Jupyter notebook as comments (I don't think this is a good way of presentation). But overall the materials in this course is quite interesting, introducing ones to Python image processing libraries.

It's a challenge course. Two things, I think they could be improved. First, when you want to show something to learners please don't do it at the bottom of the screen since the subtitle will be shown there too. Second, Jupiter notebook on the website is too slow especially for the final project. It took me more than half an hour to run my code each time. I tried to find a better way. I made my IDE in my local PC but when I tried to download images.zip, the file transfer rate is very low too.

This course is very challenging. Be prepare to do alot of outside research to complete the projects. The instructor will just give a brief introduction to the packages and expect you to read the documentation to figure out the rest. I am only giving a 4 stars not because of the difficulty but because the environment provided is too slow to complete the project. I suggest installing Jupyter and the needed package on your local environment to complete the projects instead.

Quite a change from the other modules in the speciality. The assignments are less structured than in the other modules, and more self study is required. I think that this is the main point to give students a greater experience of looking online for docs and examples.

The instruction does cover the areas required for the projects, and some additional modules that are not required for the project.

Jupyter notebook is quite different to the previous workbook.

This course is more challenging than last four courses, however, you could finish it if you remember all stuff that you learned. I think it is really helpful for develop another skills as programmer, for example: search documentation by yourself and face up to problems related with packages, etc. In general, I really encourage you to take this course if you want a challenge and learn by yourself.

The course has good content. It will help students give an idea what it takes to start using new libraries and packages in Python. The project was interesting as well. One thing I did not like was the submission format for the last project. Copy-pasting code was not a great way to submit the assignment. We could have submitted an HTML or a PDF file like we did for the first project.

Great course, material, and challenging project!

My only complaint is that I could read the notebook notes and programs without going through the lectures. Professor only read the comments of the material so after a few minutes I skipped most and just went to the material.

The notebook programs and Google are your best allies to complete this course!

Overall, I am very satisfied.

I enjoyed a lot the content of the course, it provides tremendous content and useful Python tools application. I was not able to connect with the professor methodology though. This is the first course I take from Christopher, l look forward to continuing learning from him at the applied data science specialization.

This course challenges students to apply what they learned in the specialization. However, I thought the final project could have been designed better (it was purposefully vague). I would have liked a better description of what to develop. Instead, instructors choose to use hints and forums to guide students.

Although learning image processing packages are useful, they are not of importance to me. Also, this course made it very difficult to submit final project. There is very little support from instructors or teaching assistants on the discussion forums. Needs to do a better job.

Course Could have been More descriptive. The content lack somewhere, i understand the fact that this was mainly focused on ability of students to explore the libraries & work their way out but its kind of difficult to understand. yet i like the concept & its well prepared.

The course was interesting and I liked the idea of the last project BUT the platform was unusable for this kind of project. I had to extract the resources and to do it locally. The peer to peer verification system it's slow. I give a solid 7/10. I don't regret taking it.

It is quite challenging to work with these libraries. Especially when there is no clear guidance. Among the most inconvenient things is that Kraken is not supported on Windows and OpenCV is insanely difficult to install on Mac (Apple Silicon)