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Volver a Data Engineering and Machine Learning using Spark

Opiniones y comentarios de aprendices correspondientes a Data Engineering and Machine Learning using Spark por parte de IBM

14 calificaciones
8 reseña

Acerca del 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 - 8 de 8 revisiones para Data Engineering and Machine Learning using Spark

por Minh Q N

22 de sep. de 2021

Great Course!!!


19 de nov. de 2021


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 sep. 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 sep. 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 James N

8 de nov. de 2021

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