A lecture that truly helps with understanding and applying OCR. By following along with the hands-on practice, I was able to understand the theory as well.

I learned a lot. It was very helpful.

I'm learning high-quality skills. This is a lecture that's completely worth the money. I only knew basic Python concepts like classes and inheritance superficially or could only apply them shallowly. (To the extent that I could interpret the code written by the instructor) Should I call it code refactoring...? It felt like watching a video visualization of the instructor's actual work process. I also realized that I need to learn how to utilize Python's basic built-in libraries more effectively. Especially these days, code interpretation and error handling have become relatively easier due to AI, so I'm studying with AI's help for difficult concepts or interpretations in between. Thank you for providing such a good lecture at this price.

The AI lecture utilizing Databricks seems unique, and I thank you for the excellent lecture. However, it would have been better if you had provided more overview explanations for each detailed unit. For example, it would have been even better if you had explained the functions of each menu in machine learning and supplemented how each code is utilized in which menu. Additionally, for those taking this lecture, Spark or Databricks is more of a focus, so there are likely many people who are interested in AI but lack knowledge. If you had supplemented this point as well, it would have been an even more excellent lecture.

This is a field I've always been interested in, and I was very curious about how it's implemented in other domains or in practice, which was largely resolved. It was very interesting and seems like it will be very helpful for the project I'm planning to undertake. Additionally, while some issues were resolved through previous learning about other Data Bricks or Azure related parts, I'd like to share some points where more explanation in this course would have been beneficial. 1) Methods for connecting other APIs (Section 2, Examples of connecting ChatGPT or other tool APIs instead of Azure OpenAI in the practice environment) 2) Azure VM resource allocation (Sections 3, Section 6, Required for appropriate Databricks Compute settings & Cost Budget Management) 3) Code sharing (Although live coding is preferred, this is to prevent errors when using computing resources under a personal billing model)

The reminders during the lecture to remember this as it's used in the field were good.