Great learning

Great course.

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Great course

Fundalmental

nice course

NIce Course

it is good

Very good

Very Nice

Very good

Nice!

best

good

good

none

Nice

good

good

good

good

RAS

Title: Frustrating Experience Due to Speaker and Technical Issues I enrolled in the course "Supervised Machine Learning: Regression and Classification" with high hopes, but I was left thoroughly disappointed. The most frustrating aspect of this course was the speaker's voice. At times, it was so annoying that it made it nearly impossible to concentrate on the content being presented. The speaker's tone and delivery lacked enthusiasm and clarity, making it difficult to stay engaged. To make matters worse, there were frequent microphone issues throughout the course. It felt like the words were either muffled or slurred, which not only made it hard to understand but also incredibly irritating to listen to. These technical problems seriously hindered my ability to learn and absorb the material effectively. While the course content itself was decent, the combination of the speaker's annoying voice and microphone problems made it a struggle to complete. I expected a much more professional and engaging learning experience, and sadly, this course fell far short of my expectations. Title: Valuable Content, Could Use Some Improvements I recently completed the course "Supervised Machine Learning: Regression and Classification," and I have mixed feelings about my overall experience. On the positive side, the course content was comprehensive and provided a solid foundation in supervised machine learning. The topics were well-structured, and I appreciated the depth of coverage on regression and classification techniques. The examples and exercises were helpful in reinforcing the concepts, and I did gain valuable insights into these subjects. However, there were some notable drawbacks. First and foremost, the speaker's voice occasionally made it challenging to stay engaged. There were moments when the tone and delivery were less than engaging, which detracted from the learning experience. Additionally, there were technical issues with the microphone that affected the audio quality. This made it difficult to understand certain parts of the course and was quite bothersome. In summary, the course content itself is valuable for anyone looking to learn about supervised machine learning, particularly regression and classification techniques. However, improvements in the speaker's delivery and addressing the technical issues with the microphone would greatly enhance the overall learning experience.

I'm a 45 year old software developer that never chose to progress in math beyond college algebra. I never needed it. when i took college algebra in highschool, most of the calculus related notation was not covered; this may have changed since the early 90s, but it isn't realistic to know where someone is sitting in regards to calculus. This course presented itself as if you would not need to know calc to keep up with the logic involved, but you really do need to have a good grasp on the notation involved. The frustrating thing for me is that I had to spend more mental energy just trying to digest the logic, and it really took away from the much more important content of knowing how and when to use the algorithms covered here. The math is already encapsulated in machine learning libraries, so the heavy work of trying to keep up was really mostly wasted. I think the course needs to be more up front about the math related background needed to step into it, or it should be taught with much more emphasis on applying the concepts involved in how and when to use the algorithms to create useful models. With more honesty about the background requirements to keep up, or with a shift in focus towards application, i would be giving this course a better review. but 3/5 or 3.5/5 is the best i can offer.

I enjoyed the way fundamentals were thought and it was comprehensive yet simple. I only have few complaints: 1. the practice lab and quizzes could be a bit harder and more twisted. and the number of programming assignments could be more 2. it will be great if the course focuses a little bit mode on building models using libraries such as scikit-learn rather than just explaining the theories and mathematics behind it. 3. it will be helpful to ass subjects such as cross validation, XGBoost, pipelines etc. that are essential to build an accurate model to make learners more familiar with the real word problems of training a model rather than just explaining the basics thanks for such a good content again.