Avis et commentaires pour d'étudiants pour Scalable Machine Learning on Big Data using Apache Spark par IBM

I really enjoyed this course, I think despite this it requires a review to make it more consistent. One thing that would have made it better for me. Would have been to have the jupyter notebook matching exactly the video so I could have worked on them in parallel... Thanks very well build course, it really gives a good base to start using Apache-Spark ML

The material presented in the course is important for everyone looking to go into the Data Science or Machine Learning fields, but some of the examples in the earlier chapters use Python 2 and have not been updated to Python 3. The learner has to go hunting themselves in the forums for official posts on how to fix these error (they were there).

It is a very interesting course. Some videos and lectures however should be improved:

-start with a purpose: what is the goal of this script? What do we want to learn from the dataset?

-the explanation of the sliding windows was a little bit obscure.

The scripts are useful and if the learner plays around with them she/he can learn a lot.

Although this course introduced us to the very important idea: distributed and parallel processing, but I find it too broad and too high level. We didnt go deep into any of the topics, and the assignments are to easy(some of them are already done, you just have to find the correct number for the outputs and place it in quiz section)

Interesting, but not much opportunity to practice what is taught. Instructor walks through a lot of examples, but they are hard to follow because his notebook screen is a bit blurry. A lot of type a long, and trust me, or "we will get to this latter". Pretty easy compared to other similar coursera courses I've taken.

Found this course difficult compared to others, as i am a mechanical guy. However, resources provided in this course are great. In this course unlike others requires lot of reading from resources. Finally, enjoyed this course. Only thing that troubled me is the instructors slang of English -) which is my problem not his.

Like the format of this course, which seems more laid back. Having said that, some of the assignments had some confusing portion, but need to acknowledge this is an intermediate course and not a beginner one. I also missed the part of the explanation that Apache Spark has its own tools vs. using Python's SciKit

Sources in the lectures were really great to understand what is Apache Spark and How to use it.

However, in some part of the lecture, I loss my way to understand what's going on here...

Anyway, at final course, I could review what I learned in this course and that will be a good guide to use Apache Spark.

Slides contain some typo in Python codes but highlighters are available to let you know what are wrong. However it still makes me feel that the course materials are not very well prepared.

Good thing is thing I have got a basic understanding about how Apache Spark can facilitate machine learning.

Very interesting course, learning about utilizing Apache Spark parallel processing and how to build ML models. Video quality was not satisfactory for viewing the described Python code and I had difficulties understanding the spoken language, fortunately the video's transcription helped.

Great course. There should probably be more coding tests where submissions get you a grade like some of the other Coursera coding courses.

Some of the coding in the lectures is a bit too quick, but that's probably just for because I have never used the Apache Spark syntax before.

Very good teaching techniques, The professor explained everything well, The sound quality was dull on 2nd week's video and the accent was a bit tricky for me but the quizzes were good and if you code with him you'll be able to understand the concepts easily

I like the course, but it fails to mention clearly how learning apache spark could help us. Also, it requires a certain amount of coding experience, I was able to finish it, but sometimes I had no idea what I was doing.

It was surprisingly fast-paced. There were a few intuitive leaps, including a bad data reference on the final project, that were potential stumbling blocks, but I feel more confident having overcome them.

It is a good course for beginners in the domain of Apache Spark and Apache Spark ML. Programming assignments could have been better if they were applied to "Big Data" and not on toy datasets.

Again a nice course that introduce you on Apache Spark Usage. Just a little suggestion, if you could insert a little tweak on how pass from spark to pandas and vice versa.

Enjoy :)

Teaching was clear and understandable. Only feedback would be I hope the lab work would be more hands on because I'm worried I don't pick up the concepts unless I type them out.

Depending on the student, this can either be an easy or a difficult course. Some parts needs update, and it would be great if there are more explanation on the algorithms.

Nice introduction to Big Data processing, No coding skill required. A little more focus on the theory would be nice as the Python coding exercises are a little redundant.

Great tutorial overall.

Room for improvement: Fix the differences int the definition of kurtosis and skew between vide, test, examples (preferable the scipy definition).

It was a very interesting and skillful course. Thanks to IBM and Coursera for such a wonderful course. Special thanks to Mr. Romeo Kienzer for explaining it so well.

There are some issues with the normalization of the distribution moments. Everything else is good material to learn how to use apache-spark for the first time.

Great notebooks. But the videos are getting old and are a bit obsolete compared to the contents in notebooks. I would have also appreciated more theory.

Learning material is pretty good for getting started with Apache SparkML. Everyone who leaps into Scalable Machine Learning this is one of your choice

I found this course incredibly beneficial. Moving forward, I would like to see a bit more explanation of concepts and few extra workable examples.