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Отзывы учащихся о курсе Machine Learning Data Lifecycle in Production от партнера

Оценки: 314
Рецензии: 59

О курсе

In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types...

Лучшие рецензии

2 июля 2021 г.

Interesting material. There are quite a lot of typos and many code snippets are directly from the tfx manual pages however the instructions provided and logic of the course is clear.

13 окт. 2021 г.

It is a very informative course. I learned a lot about data, metadata, schema and feature engineering, Also, Robert Crowe sir is a very good teacher.

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51–68 из 68 отзывов о курсе Machine Learning Data Lifecycle in Production

автор: Jacob W

12 янв. 2022 г.

A comprehensive course. My only criticism is that in some videos the pacing is inconsistent where half the video is reviewing what will be covered and then it is very quick to go through the actual content.

автор: Carlos A L P

25 нояб. 2021 г.

I​ liked the intro to several techniques for feature engineering, validate anomalies between training and serving dataset but sometimes the labs didn't explain in details the steps implemented in the code

автор: Wanda R

27 июля 2021 г.

It's a new course so sometimes there are mistakes in the translations or there is something off in the assignment's grading, but the content is great. :)

автор: Umberto S

15 авг. 2021 г.

Really practical course with good examples and a lot of materials on MLOps and examples on TFX to build and manage ML Pipelines.

автор: Shayan H

13 окт. 2021 г.

The course is exciting. Lab and exercises are informative, but the answer to the quizzes are a little ambiguous.

автор: Hassan K

5 авг. 2021 г.

It will be more interesting if unstructured data such as image, audio, ... is used more in the course.

автор: Choo W

15 авг. 2021 г.

useful insights, but tfx implementation might be invasive towards exisiting mlops pipelines

автор: Khaerul U

30 дек. 2021 г.

course material very good, but instructor very rare give example that make sense to me

автор: Bharath P

29 мая 2021 г.

excellent course. Nice to see how we can detect data drift and skew drift

автор: Thiago P

5 янв. 2022 г.

Really nice course, but too much focused on only one framework

автор: Gonzalo A M

27 окт. 2021 г.

Sometimes this course is a little boring

автор: Enrique C

4 янв. 2022 г.

Good intro but it looks like in other courses from, while they teach you something, they also try to "sell" people a specific framework. In this case, they seem to be selling TFX, whose API seems to be in constant flux with no guarantee (maybe not effort at all) of backwards compatibilty. It is very likely that if you download a notebook and try it in your computer, unless you're using the same library versions, it would not work. Some quizes seem to be not in sync with the lesons content (questions are about the content off the next session). not acceptable for a platform like Coursera that has horrible customer support and that is ruthless with users that have issues with their payment method.

I still recall how they sold people the Trax library in the NLP specialization which seems to have replaced Trax with huggingface. I take what is useful from these courses but I distrust their agenda.

автор: Carlos C

24 окт. 2021 г.

It is too much Google oriented

автор: Reto A W

10 нояб. 2021 г.

I was not happy with the course. In the part 1 the lecturer showed a lot of real world example of developing big ML-systems. The lecturer for this course is more a library creator than a user of it. And therefore also it feels like an advertisement for tensorflow. Which is an odd combination for me. So it does not teach a lot of useful theory because it focuses on how tensorflow manages pipelines and not a lot about the concepts. But also the programming examples are very artificial examples taken from the tensorflow tutorials or documentation. What I liked in the first course was the practical view on a specific problem. The programming exercises I also did no like because I did not learn anything useful. I only "learned" to use tensorflow a bit. But the concepts implemented are so basic that they are not interesting at all. I am aware that this has to be like this if we are not expected to program for two day but I don't see the benefit for me of solving mandatory useless exercises. The result of this was: I was skipping through the videos in 2x and was solving the quizzes as fast as I could. Speaking of quizzes. There were quizzes asking questions never mentioned in the videos and once the quiz was posed before the video where the things were explained. Also the quizzes used unusual wording for concepts plus not clearly written questions. In the end there were some useful insights here and there but it was quite an effort for me to filter them out as my motivation was lacking after some time.

автор: Nikki A

11 янв. 2022 г.

I was pretty disappointed in this course, particularly compared to the previous Andrew Ng course in this specialization. The last course was very informative and general, where as this one felt like a sales pitch for TFX. I learned very little, especially since my focus is on deep learning, not the shallow, tabular data that was discussed here.

автор: Max A

17 сент. 2021 г.

The course is informative and well made, but the bugs in the grading algorithm are super annoying!

автор: Merlin S

4 июля 2021 г.

I​ts incomprehensible to me why this course has such a good average score.

автор: Germán G

28 мая 2021 г.

Traté en varios navegadores de enviar mi trabajo para ser sometido a evaluación, sin éxito. No obtuve respuesta ni soporte.

El contenido es interesante pero el soporte y habilitación no está al nivel de lo requerido: es lamentable que no reembolsen.