Computer Vision in Python for Beginners (Theory & Projects) (Udemy.com)

Impressive, This complex subject matter is structured in a manner that makes it easily understood. The modules combine theory with how to apply the theory in real-world applications. You have taken complex material and made it easily understandable. I thank you for creating this course.

The course starts in an interesting way, the treatment of images even if a bit long was clear and useful, then came the tribute to matrices and from interesting it became tedious. I have absolutely nothing against matrices and their usefulness so much so that I bought specific courses that concern them including one in which the project of the entire course was to explain three-dimensional transformations by creating a library in C that was EXTREMELY INTERESTING because it was oriented to a purpose that was consistent with the topic of the course. What I don't think is right is to hold people's attention and motivation hostage for a topic, pushing on lateral academic treatments even when these ultimately become deleterious. The example that summarizes this dissatisfaction is represented by Section 5 which is a treatise on matrix eroticism lasting 2h + 20m, which ends with a 5 minute video in which in two lines of code the OpenCV library is used for its real purpose, that is, to make an operation simple, efficient and intuitive. Honestly, if you add the duration of sections 3, 4 and 5, you are talking about 8 hours out of 26 total, it is 1/3 of the course, moreover it is positioned at the beginning and becomes a wall to be liked by force to continue on what really led us to choose the course. I give a piece of advice to the authors, the explanations of those sections are not wrong and can be useful, but move them to the end as an optional appendix for those interested and create a new streamlined section on transformations applicable to images, using only the already existing tools made available by high-level libraries.

In this course, the instructor presents in depth the fundamental building blocks of computer vision. From the structure of images, image transformations, corner and shape detection,... Most of the notions studied here are explained efficiently by describing precisely the mathematical theory behind them, while implementing them from scratch with numpy. With further work, the student may be able to implement most of the famous tools (which do not use deep learning) available in cv2 for tracking, detecting objects,.. with only numpy and matplotlib. He will also be able to use them more efficiently by customizing them. The course contains also many quizzes, and problems. I definitely recommend it to everyone interested in learning the fundamental building blocks of computer vision while being able to implement them in numpy, and developing an expertise in practical projects.

This was really best course for me. For beginners I recommend don't jump directly on Library like OpenCV, YOLO and others. get the basic which are Available in this course. I think the tutor went very well from the scratch to depth. End of last few modules gives an overview of prebuilt tool in python. It is really best course of getting basic In-depth. Thank a lot of AI Science and mainly the tutor for providing this very nice course.

Very interesting, perfectly structured, very detailed, and, most importantly, an amazingly explained course! To be honest, I wasn't expecting much of a course on computer vision on Udemy. I have some experience with the original OpenCV course for beginners, so I can 100% say that the AISciences team has definitely made a sweet candy. Much appreciated. Highly recommended.

Topics covered in the course are excellent. The hands on provided along with theory makes it even better. If you want to understand how Computer Vision algorithms work behind the libraries and get a good hold on applying different techniques to applications like stitching, calibration, perspective transformation etc., this course is a one stop destination for you.

I'm am still working through Section 3, but I find the lectures/videos very informative. The lecturer is knowledgeable about the concepts as well as the practical application for each concept behind Computer Vision. I enjoyed each video and I look forward to incoming ones. I am giving this a 5-star, because other than the knowledge and skills I've learned, the materials are well prepared and they gave it a lot of thought and effort to come up with the entire course. I had a quick look on the 3D Reconstruction videos, I would be interested if there is an entire course on that.

Many data scientists are mostly package runners. Unfortunately, I (partially) belong to this group. On the contrary, the tutor of this course really dug deep into the nitty-gritty of the algorithms and hand-implemented them. I gave it 5 star so more people can see this.

The course sounds very interesting. The instructor is obviously well versed in the topic(s). I never felt this much at ease with the topics in Computer Vision but the details with which things are discussed in the course and how things are distributed throughout the course, that blend is just Amazing. Looking forward to completing the course.

What I like most of this course is that EVERYTHING is deeply explain in detail. And what is not explained, a link or help is provided. Every insight gained is applied further in the course. The other thing which I really appreciated is you can make the very same examples, side by side with the teacher, and you will get the same results (and errors). Don't overestimate making errors: you'll learn more from your mistakes than your successes. Even though, 'no prior knowledge' for this course would frustrate a segment of beginners. If you have few or all of this concepts, the course will be easier: - Python (basic understanding and above) - Jupyter notebook (easy to install, learn the basics - 10/15 minutes) - Matemathic concepts (specially matrix operations), which leads some previous knowledge with some Python libraries (numpy). The course is slow paced, building concepts one on top the other. When you least think about it, you find yourself doing amazing things. Highly recommended.