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Avis et commentaires pour d'étudiants pour Scalable Machine Learning on Big Data using Apache Spark par IBM

3.8
étoiles
1,177 évaluations
301 avis

À propos du cours

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) https://www.coursera.org/learn/python-for-applied-data-science or similar https://www.coursera.org/learn/machine-learning-with-python or similar https://www.coursera.org/learn/sql-data-science for optional lectures...

Meilleurs avis

AC
25 mars 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.

CL
11 déc. 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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176 - 200 sur 300 Avis pour Scalable Machine Learning on Big Data using Apache Spark

par Aniket A

20 oct. 2020

This is a quick, on-point introductory course for Apache Spark, not extensive, since not many industries are going for it, but the necessary concepts like Extract, Transform and Load also know as ETL is explained very well. The instructions are bit scarce, but the notebooks helps a lot. Overall, good!

par Jesus M G G

26 déc. 2019

-Some videos seem outdated, and one of them doesn't have all subtitles.

-The exercises sometimes uses some models or functions not covered in the videos

-I had some issues connecting to the Spark Kernel (it was working before and then stop working. It fixed it self after a few days)

par Shivakumar K H

22 avr. 2020

I felt that the course was filled with practicals which was explained very fast and without proper explaination. But the overall content was really good. It must have been more than 4 weeks and with proper explaination on coding part and including more related theories.

par Mohammad H

6 avr. 2020

i like very much the Machine Learning, but the course was focusing to cover the whole functions,methods,logarithms...

but i was preferred to focus on few concepts and do more practicing on to understand more the course and to make it more beneficial in our job carrier.

par Frauke S

19 mars 2021

The course material seems to be copy and pasted together from other courses and the versions/classes/functions of the software have changed compared to videos and assignments. Nevertheless I got a first impression on how to work with Apache Spark/pyspark hands on.

par Dylan W

21 mai 2020

I think this is a fine introduction to Apache Spark, but the notebooks don't really require much thought to complete. It'd be nice if they were a bit more instructive. And I'm not a big fan of lecture videos just showing the instructor type the code.

par Scott P

27 févr. 2020

The course material was clear but we are never really given any challenging practice exercises to do. The "project" at the end was litterely just running prewritten code - it would have been better if we got to write the code on our own.

par José C

17 nov. 2020

It seems that the course is not updated for some time.

What is described in the videos not always applies to the exercises.

Some mistakes are corrected with labels on the video.

In general the quality of the materials is not great.

par Rashmin D

22 avr. 2020

Its a good course but it duplicated content from the previous course in this specialized certification. Also speed for writing code is too fast in video. But some APIs and exercises are really good.

par Mohamed A A

24 mars 2020

Overly, It is a well structured and oriented course, especially the practice part. However, the lectures could have been improved and made clearer. Thank you for all your efforts.

par Andres I C R

9 nov. 2020

The objectives on each class were not clearly defined, and the videos have a lot of errors that are partially corrected but will be good if the content of the course is reviewed.

par 汪揚

11 déc. 2020

Lectures video and practice seems not consistent. Some of the contents in the video needs to be updated, since the interface has changed in IBM studio.

par Lior D

24 févr. 2021

Tutor was definitely knowledgeable, but i felt the course was more like a code review and more emphasis should be given to teaching/explaining.

par Jesus C

12 juil. 2020

The course teaches the very basics in a pretty simplistic manner. I think both videos and labs could be more descriptive and illustrative.

par César A C

26 avr. 2020

Very precise examples of parallel process to make predictions but may be not as demanding as other courses. The exercises where too easy.

par Jasper v H

24 juil. 2020

Good introduction to Spark, but very little playing around with ML.

Also, the UI for IBM Watson keeps changing and is really frustrating.

par Diego D

12 juil. 2020

This course is outdated, and there are a lot of errors in the presentation.

I think most of the videos in the course need to be updated.

par Cristian M

6 nov. 2020

Quite difficult to understand, since the videos have plenty of errors and the tutor goes really fast with no explanations some times.

par Shivam S

22 nov. 2020

Not enough coding opportunities provided. More Coding assignments and practice will be better and more content is very much needed.

par Sanders L

19 nov. 2020

Course needs some polishing. Video content seems to be outdated and not delivered in a format consistent with other IBM courses.

par Ratnakar M

16 janv. 2020

Content was ok , IBM has better course production than this , sorry to say , i m very grateful for the effort

tutor took . Thanks

par TJ G

11 janv. 2020

This deeply need a much more detailed course on Apache Spark. You need far more than this course to actually get into PySpark.

par Binod M

11 août 2020

Good introduction but seemed rushed and felt like it had lot of gaps . But the explanations that were given were very nice

par Bear B

22 janv. 2020

Hard to listen video without subtitles.

It be better to show how create a notebook in the watson on the first lecture.

par ARSHAD S A

27 juin 2020

It would be nice to have an updated course content video. Other IBM courses are much more updated and interesting.