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.

These are very effective courses with hands on practices that will give you a deeper understanding. However, the VM tools are outdated. These do not work, so you have to workaround for the alternatives, if you dare to take the challenge of completing this course ;). Happy Learning!

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.

Have to make sure that code provided are fine and without problem... also, better instructions on how to go about the tool needed to use. Remember, we are learning and the amount of tools in Big Data ecosystem is vast...

Interesting material. Ran into several issues with the hands on that could have been avoided. Loved learning more about Neo4J. The section on Spark needed more time and additional descriptions.

The course is excellent, the hands on allow are realistic and allow you to have contact with the analysis tools and real Big Data applications. I am very happy and satisfied with the course.

Reasonable overview. The VM environment is a major challenge for my hardware. Takes more time to make it work than it should. I am wondering if a cloud solution e.g. GCP would be better.

Great overview about the machine learning in general. There are still lots not covered specially the Neural Network algorithms. Learning Spark MLlib was great advantage of this course.

The fact that the assignments are graded means that there’s incentive to work on them, solve problems, and ask questions. Traditional online courses don’t offer that incentive.

Hands n exercises and corresponding quizzes are great !Content could be more detailed, but may be I felt it so given my past exposure to ML. I enjoyed learning Knime and Spark.

The precise definitions for many commonly used terms were very helpful. You do not find these details in many books or documents. Also, using KNIME was also interesting

Good course, but I found several problems in the virtual machine and it was difficult to solve them with the forum info. I had to rebuild the vm several times.

Good overview of working with SPARK and KNIME - acceptable little theoretical background for all the presented concepts for the sake of application use.

This is a good course to understand how we can apply basic ML algorithm like classification, clustering using KNIME and Spark ML on very high level.

Good, would be better if was in portuguese. and sometime is very painful configure the machine. But is a good course, better than the previus 3