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Volver a Scalable Machine Learning on Big Data using Apache Spark

Opiniones y comentarios de aprendices correspondientes a Scalable Machine Learning on Big Data using Apache Spark por parte de Habilidades en redes de IBM

1,243 calificaciones

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This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer. Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner. Therefore an applied knowledge of working with Apache Spark is a great asset and potential differentiator for a Machine Learning engineer. After completing this course, you will be able to: - gain a practical understanding of Apache Spark, and apply it to solve machine learning problems involving both small and big data - understand how parallel code is written, capable of running on thousands of CPUs. - make use of large scale compute clusters to apply machine learning algorithms on Petabytes of data using Apache SparkML Pipelines. - eliminate out-of-memory errors generated by traditional machine learning frameworks when data doesn’t fit in a computer's main memory - test thousands of different ML models in parallel to find the best performing one – a technique used by many successful Kagglers - (Optional) run SQL statements on very large data sets using Apache SparkSQL and the Apache Spark DataFrame API. Enrol now to learn the machine learning techniques for working with Big Data that have been successfully applied by companies like Alibaba, Apple, Amazon, Baidu, eBay, IBM, NASA, Samsung, SAP, TripAdvisor, Yahoo!, Zalando and many others. NOTE: You will practice running machine learning tasks hands-on on an Apache Spark cluster provided by IBM at no charge during the course which you can continue to use afterwards. Prerequisites: - basic python programming - basic machine learning (optional introduction videos are provided in this course as well) - basic SQL skills for optional content The following courses are recommended before taking this class (unless you already have the skills) or similar or similar for optional lectures...

Principales reseñas


25 de mar. de 2020

Excellent course! All the explanations are quite clear, a lot of good quality information provided from amazing teacher. Additionally, response times for any question is very fast.


11 de dic. de 2019

Really really REALLY enjoyed this course! The instructor does a masterful job of going from simple examples and building up complexity in a very logical and thorough way.

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251 - 275 de 317 revisiones para Scalable Machine Learning on Big Data using Apache Spark

por Nils N

24 de mar. de 2020

Maybe I do not have knowledge about Python, but a lot of things were not understandable for me. In addition, parts of the course are still shown in an older, out-of-fashion version of Watson. The shown code is not working in todays version

por Nazmul H

20 de dic. de 2020

This course and assessment method is not standard enough. It should have more practical exercises such as RDD programming, ML, SQL. Course project should have related to developing mini big data application with RDD to ML prediction.

por Claudio C

27 de abr. de 2020

The course should be reorganized. The video are taken from different courses and is not fluid follow it. There is very little in programming with functional programming. There are many concepts but not well explained. Not advided!

por Juho H

23 de abr. de 2020

The course teaches important concepts and skills on how to scale your machine learning algorithms - but it is in a desperate need of an overhaul to fix the numerous errors in the videos and workbooks.

por Camilo F P

9 de sep. de 2020

Did not like it that much, I´m not clear about spark and big data use cases for ML. The themes were variated and did not follow a line or path of learning.

por Esteban E

28 de ene. de 2020

Not clear at all.

A lot of things are not explained, or explained in a confusing way. I learn more by researching what things meant than from lectures

por Friscian V C

7 de jul. de 2020

I dont like the instructor very much. I feel like his explanations are not the best and everything was just too fast.

por vikram s

3 de jul. de 2020

It's very difficult for a beginner (like me) to understand the whole science behind the concepts in Apache Spark

por shiva k P

11 de ene. de 2021

The content is quite old and full of mistakes. It would be great if the course material quality is improved.

por suman k s

17 de may. de 2020

Explanation not satisfactory and exercise also not so good.

too much issue in setup all these exercises.

por Billy

16 de ene. de 2020

focus too much on practical skills than the balance of concepts and implementations

confusing to follow

por DK N

20 de ene. de 2021

It was fine, but i couldnt understand clearly what the instructor want to explain.

por tamador a

3 de jun. de 2020

The course should give more in-depth assignments and also more explanation.

por Harsh K

12 de abr. de 2020

There is a lot of audio problem and content is also not updated.

por Branly F L

14 de feb. de 2020

This course needs more spark towards the student.


por Victor B

22 de mar. de 2020

Videos are not informative.

por kexin w

1 de ene. de 2020

A lot of errors in lecture.

por Kirivitige A S F

7 de abr. de 2020

So many errors in the codes. Especially the ones the instructor is showing us in lecture (his files run on python 2.7 and i'm running on python 3.6- has not updated some programs to run on python 3.6 with spark 2.3). He doesn't specify which file at the beginning of the video, nor does he have a link to the sample code he is showing us, nor does he specify which file to insert a spark session and to where can we find that specific file in GitHub. It's a huge confusion for a person who has zero programming knowledge and It took me a lot of time to fix the errors in the codes to get back on the lecture. I am utterly disappointed with this section. Didn't have any issue with the last session of this course. I wasted a lot of time. I'm utterly disappointed with this course.

However I must appreciate his lecturing is excellent. I was able to fully understand the theoretical part he explained. I did however fail to quickly understand the programming aspect due to multiple errors in the code.

por Alistair K

4 de jun. de 2020

In a word - Abysmal, the first indication of the quality of this course is the intro video which the lecturer filmed in his car!! Very unprofessional - he explains that he doesn't work from the office much, but surely he could put some effort in...

Things don't get better, a number of in-video quizzes simply offer 2 possible answers - "OK" and "OKOK", the majority of lectures are simply the lecturer typing code with little explanation.

And don't get me started on the quality of his code - possibly some of the messiest code I've ever seen, inconsistent style, massive blocks of empty lines....

If it wasn't for the fact I am doing the specialisation, then I wouldn't touch this course.

por Narasimhan, S

21 de ago. de 2020

At the end of the course the only thing i could say is for a professional certification this course falls short of all parameters. Be it courseware, assignments, the way it was delivered etc were all too high level and didnt really make me enjoy it. The least i say about the instructor's english slang which was very difficult to understand and the coding part was too fast.

The big data methodology and its applications has some really good use cases which i think needs to be covered as part of the course and the course in itself has to be revamped at its best.

I reiterate for a professional certification this falls short and i was disappointed.

por Samuel K

19 de ene. de 2020

This course has a lot of room for improvement (not to say plainly it's a waste of time). The video lectures are useless. They consist on the instructor coding some lines to show basic commands in Apache Spark. An introductory course on Apache Spark would be much more useful than this one. The basic stuff on regression and classification methods is really poor as well. The Quizzes and practice exercises only teach some basic Spark functionalities, which could be the only somewhat useful elements of the course. Avoid this unit if you can, I just took it because I enrolled in the AI Professional certificate on Data Engineering.

por Gopal I

14 de may. de 2020

This was a poorly written course that did not explain much of the Spark fundamentals. It was really hard to understand the instructor's line of thought, added with bad instructions and poor resolution videos (tried changing video settings too). In addition although IBM Watson was supposed to be free somehow this ended up as a metered service (different from Course 1). Instructions again were not updated. This course needs a complete revamp.

por Leon B

14 de may. de 2020

Learn about the diffs between rel-databases, no-sql and blobs on disk. Learn (again!) what mean, stdev, median and kurtosis mean. Plotting with MatPlotLib, brought as the 1st world wonder.

Intro to Spark and your final challenge: execute a provided notebook and just copy-paste the results.

No need to think, just know how to apply CTRL-C & CTRL-V in the end.

Al in all, can be done on a rainy Sunday afternoon instead of 4 weeks.

por Alexander D

12 de mar. de 2020

Don't take this course. It's poorly made, and should not be a part of this specialization unfortunately. I do like the subject matter, but the IBM cloud framework is a headache. I struggled with it and barely passed the final project because some cells wouldn't run.

I did do what I could to absorb the information by looking at the notebook and testing myself, but to be honest, I just wanted to get it over with.