It is very helpful to me .

Amazing course! thank you

completo y satisfactorio

Very good content

Best n all

Gracias!

its good

Amazing

good

good

A fast-paced overview over Python fundamentals with ML in mind. Hardly anything practical, so most suited for people who want to get a refresher or already know the core principles and want to transfer their knowledge to how it is done in the Python world.

One of the best Python courses that I had done, my background is in Data Science not in Engineering. I think that is an advance course since a vast number of concepts are well explained so quickly.

It is a good introductory course to the topic of mlops, although in the last week it is a bit dense since there are topics such as FASTAPI or FLASK or DOCKER, which require prior knowledge

The Courser covered a lot of things with keeping the number of videos low, but python environments in some of the labs were not already created.

Some more advanced Python (Flask, FastAPI, Azure, etc.) could have been explained in more depth and detail with practical labs.

Good (although basic) overview of python and essential libraries (numpy, pandas, ...).

Me parecio super enriquecedor el contenido del curso.

Good but week 5 content should be more detailed.

A little bit too fundamental for skilled MLE.

Se notan muchos cortes en los videos y errores que ocurren en el codigo (durante el video) que el instructor no explica. Tambien hay detalles en la forma de organizar el codigo que no se explican apropiadamente. Creo que es una buena primera aproximacion a un curso introductorio pero que deben poner mas atencion a los detalles de como diseñar el codigo (incluso si se trata de un codigo básico).

This course provides a good overview of toolset and techniques involved for building machine learning applications. I enjoyed the most, when several methods where presented for the same goal. It is up to the engineer to decide for the approach that works best for him.

A rich enough course, but a bit unpolished. The labs are often either missing or not connected to the syllabus, there are topics assumed known (e.g. Containers and Docker) and there are minimal instructions for the labs. A lot of potential, but some polishing needed.

Module 5 feels random and out of structure. Weak instructor without teaching experience.

More exercises involving coding would be nice

While there is some useful information in this course, it is buried in a huge amount of noise. The whole thing is poorly organized. Some material of marginal relevance is covered multiple times. (OK, I get how a makefile works. Again.) Other things are laughably out-of-scope or out-of-sequence. I don' think somebody half way through a course on machine learning operations needs to spend five minutes learning what a variable is. And many of the "lessons" literally consist of the instructor recording himself while hacking his way through improvised programming projects. For hours. My advice: don't waste your time. Maybe glean what topics are covered, and then try to learn them on your own. Virtually any technology mentioned in this course provides free tutorials that are better organized and more informative than what you will find here.