good

good

Good

Good

Good

Good

none

good

wow

Gud

5/5

NA

There were several issues running the hands-on assignments; problems with getting various python tools and/or features. These issues made the labs frustrating at times, take much longer than needed, and quite stressful to complete.

This is an old specialization and must be updated with a variety of necessary modifications done to it in order to keep it running!

Se abordan conceptos teoricos muy importantes pero el uso de pyspark en algunas oportunidades es complicado ya que no se da una introducción al tema, por lo q se da por sentado que la persona que hace el curso debe conocer pyspark. Adicionalmente me parece interesante instalar pyspark en mi propia maquina sin tener que usar una Maquina virtual de cloudera.

Good overview of tools specially Spark. The last demo handson with Spark clustering had too much content to be covered in 11 minutes. The presenter does not give any details on many functions he was using. Felt like copy paste coding. The rest was good, specially the lecturer compared to the lecturers of the other courses of the specialisation.

Lot of problems with setting up the virtual machine initially, but these can be resolved by doing the exercises on your own local computer instead. The exercises are great! Very fun exposure to ML libraries. I found myself using sci-kit libraries instead of the Spark libraries to complete the exercises, and it worked out just fine.

The entire coursework is very well explained and organized such that we will get better understanding of the terms related to this field. Hands-on exercises have also given better insight of how to use those tools. I would suggest to take this course for getting a brief knowledge about Machine Learning and Big Data.

The content of this course is very useful. I really enjoyed it. The only problem I had was the possibility to work with an instance of Apache Spark in my laptop. This machine couldn't initiate the instance because of its capacity. I think they should recommend other online utility for the hands-on practices.

This course is probably a little bit too simple for anyone with a basic background in machine learning. The introduction to KNIME was unexpected but a nevertheless welcome addition. The Pyspark course material could do with updating to reflect changes in a few python libraries.

Only one concern that was faced during the course is, unfortunately, all the needed spark related libraries are not available to setup. It would have been nice, if either online compilers or readily accessible along with the course, would have become learners journey smooth.

Un excelente curso, para aprender ML, y buenas herramientas, aunque el uso de Cloudera para Big Data es pesado para procesar incluso como VM. se refuerzan conceptos. aunque algunas veces si es bueno detallar algunas etapas como la normalizacion que es muy importante.

very good introduction to the topic. I enjoyed the hands on exercises but wanted more explanations and may be more reading/exercise and in depth to pyspark. Overall I would recommend this course for anyone like me just starting to scratch the surface on chine learning.

This course is really nice and interesting one. Dr. Mai is excellent instructor and has good capabilities to simplify complex ideas. However, I did disagree with the course name since a main subject of this course "big data" is not touched in the given sample dataset.

Non working libraries are there in cloudera. I got lot problems to install jupyter and ananconda therefore I used my os and installed everything indivisually. Please solve that. Anyway other things are awesome. Good Job. Thank you very much for providing this course.

Well explained topics about Machine Learning in Big Data scenarios. It's difficult to install Pyspark because of an outdated Cloudera version, but searching in forums and installing Cloudera VM 5.5 you can complete all tasks and assignments.