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Voltar para Data Engineering and Machine Learning using Spark

Comentários e feedback de alunos de Data Engineering and Machine Learning using Spark da instituição IBM

3.5
estrelas
15 classificações
9 avaliações

Sobre o curso

Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others. In this short course you'll gain practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression, Classification, and Clustering. The course culminates in a project where you will apply your Spark skills to an ETL for ML workflow use-case. NOTE: This course requires that you have foundational skills for working with Apache Spark and Jupyter Notebooks. The Introduction to Big Data with Spark and Hadoop course from IBM will equip you with these skills and it is recommended that you have completed that course or similar prior to starting this one....
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1 — 9 de 9 Avaliações para o Data Engineering and Machine Learning using Spark

por Minh Q N

22 de Set de 2021

Great Course!!!

por ENUONYE D J

19 de Nov de 2021

good

por David S S

15 de Nov de 2021

I can't rate higher this course due to the problems with the final project... I hope all the errors could be fixed for future students because the course is excellent and the exercise is great to practice all the knowledge acquire but it has a lot of bugs.

por Natale F

25 de Nov de 2021

The Data Engineer part is too fast. The final assessment focuses on the implementation of Machine Learning algorithms with Spark, there is no Data Engineer code production required.

por Sheraz M

18 de Set de 2021

T​he final assignmnet instructions are not very clear and also there are some coding msiatkes that lead you to unexpected results.

por Dmitry K

14 de Set de 2021

Peer project has tasks which has never been though or referenced. Part of the labs are failng with lack of resources and git has some obsolete code.

por Cristina M M

9 de Nov de 2021

The theory and practice of this course are not at the same level. Yo need to learn some statistics and ML theorical concepts previously.

Labs cannot be do it only with the explanations of the videos.... The final project shouldn't be the place where you see a decision tree.

Also, there is a some commands that work in a bad way in the labs. I think the course need a complete revision, keeping in mind that a lot of learners do the course as part of a certification and had no experience with ML and a only a little with spark.

por Omar H

5 de Dez de 2021

It offers very little information, The labs are not well explained, this course doesn't add any value for the specialization.

por James N

8 de Nov de 2021

Assignments remain offline for more than a week. No refunds offered, no staff responses