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Learner Reviews & Feedback for Python Essentials for MLOps by Duke University

4.2
stars
228 ratings

About the Course

Python Essentials for MLOps (Machine Learning Operations) is a course designed to provide learners with the fundamental Python skills needed
to succeed in an MLOps role. This course covers the basics of the Python programming language, including data types, functions, modules and
testing techniques. It also covers how to work effectively with data sets and other data science tasks with Pandas and NumPy. Through a series
of hands-on exercises, learners will gain practical experience working with Python in the context of an MLOps workflow. By the end of the
course, learners will have the necessary skills to write Python scripts for automating common MLOps tasks. This course is ide...
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Top reviews

HH

Feb 4, 2025

Python best practices across development, testing & wrapping using API was well covered. Lectures on Python API frameworks can be more in detail.

RK

Jan 9, 2024

Amazing, if you have some python basics, but even if you don´t it gives a great overview. Well done :)

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26 - 50 of 59 Reviews for Python Essentials for MLOps

By Tharun k G

May 9, 2024

Exactly what I was looking for, Thanks a lot!

By Aditya D

May 25, 2024

Good course for getting started in ML OPS

By Kasthuriraajan R

Mar 4, 2023

It is very helpful to me .

By James E V

Jul 23, 2023

Amazing course! thank you

By Vania G

Aug 20, 2024

completo y satisfactorio

By Girish C S

Dec 10, 2024

Very good content

By Abdul J

Aug 8, 2023

Best n all

By Takahide M

Feb 10, 2025

very good

By YUQIN L

Aug 18, 2024

Gracias!

By Pradeep

Jan 4, 2024

its good

By uuTeam

Jun 3, 2023

Amazing

By Deepika M S

Jan 2, 2025

good

By Vaishnavi K

Apr 21, 2024

good

By Claudius S

Oct 20, 2023

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.

By Jesse R

Jan 21, 2025

I really enjoyed the technical depth this course. However, the labs at the end are too vague and I need concrete examples to work with. Perhaps use the examples introduced through each module and have students access sandboxes earlier on.

By Marcela H B

Jun 9, 2023

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.

By Julian G C M

Apr 2, 2024

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

By Kartikey c

Aug 22, 2024

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.

By AG S

Jun 13, 2023

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

By Jacques L C

Dec 11, 2024

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

By Victoria R M C

Mar 31, 2024

Me parecio super enriquecedor el contenido del curso.

By S K

Nov 13, 2023

Good but week 5 content should be more detailed.

By JASON C

Dec 20, 2024

A little bit too fundamental for skilled MLE.

By Facundo M G

Jul 17, 2023

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).

By marco m y p

Oct 4, 2023

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.