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学生对 IBM 提供的 Scalable Machine Learning on Big Data using Apache Spark 的评价和反馈

1,224 个评分
310 条评论


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...


Mar 25, 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.

Dec 11, 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.


226 - Scalable Machine Learning on Big Data using Apache Spark 的 250 个评论(共 312 个)

创建者 Wenbo Z

May 26, 2020

The contents are not well-organized and sometimes confusing.

创建者 André S M

Aug 1, 2020

The course is outdated. exemples in old version of spark


Jan 6, 2021

Good content but explanations are not always very clear

创建者 Xueling L

Jun 10, 2020

Video is too blurry and so is the content of course.

创建者 Ameya K

Jan 11, 2021

Multiple errors in the instructional videos.

创建者 Panya S

Nov 14, 2020

His accent is very difficult to understand.

创建者 مجید د

May 24, 2020

course video's need a complete revision

创建者 Aditya K

Aug 4, 2020

The content is not detailed enough

创建者 Gao S

Dec 21, 2019

Instructor accent is strong

创建者 Axel A

Aug 22, 2020

Mejorable Course Materials

创建者 NoOneMine

May 12, 2020

Pls improve sound quality

创建者 Linda A L

Jun 30, 2020

Difficult to follow

创建者 Hamad

Sep 26, 2020

Too Easy...

创建者 Tarun C

Mar 14, 2020

I felt this course was a bit too light. Romeo does reference some other more advanced courses which I will definitely check out. I did not feel like I learned much in this course for two reasons: 1. the lectures were kept pretty high-level and 2. the exercises and final quiz required almost no work or thought to complete. I learn best by doing; so for the final quiz I would have preferred if instead of being given all the code we were given the (cleaned) data set and then asked all the relevant questions without having all the code prepared for us. It forces us to figure out how to implement what we've learned and search the Apache Spark API. That being said, I did like Romeo's teaching style so I'll check out more of his courses.

创建者 Marc D

Mar 5, 2021

The course is quite easy to understand. However, the presentation of the videos are not good. There are a lot of mistakes in the demo videos and is just addressed by adding some sudden pop-up bubble comments in the video without getting any explanation. There are also outdated codes that doesn't immediately work when you try doing it yourself. The video resolution of the demos in the notebooks are also very low. I tried increasing the resolution of my video but the notebook is still very difficult to read.

创建者 Oakleigh W

Nov 9, 2020

The first week is okay; a good introduction to how Apache Spark works to parallelise computations. However, from then on code is poorly explained, and videos need updating to reflect current Python syntax. The fact that there are alot of pointers to external github repos with 'correct' code makes it difficult to learn. This course is not to the standard of others in this IBM AI Engineer path. The last week only ends with a 'fill-in-the-answers' quiz from a prewrote notebook.

创建者 Cristina G

Apr 14, 2020

Unfortunately, there seems to be quite a few errors in the course. The only skills that you can actually take away is how to use Apache Spark. The machine learning and evaluation metrics explained in this course are riddled with errors. When writing to the teachers the only thing they say is they are checking on it and will get back to you and never do. I usually really like the IBM courses but this one was by far the worst MOOC I have taken so far.

创建者 Gaby B T

Apr 6, 2020

One of the worst courses I ever had.

1 - The whole thing seems rushed. A lot of mistakes!

2 - Confusing slides and exercises.

3 - Useless quizzes that provide no additional benefit to the learner.

4 - Uncompleted transcripts under the videos.

I do not recommend this course. Unfortunately, I have to complete it for a specialization, otherwise, I would have abandoned it.

创建者 andrew r

Nov 22, 2020

Out of date and confusing examples. Watson studio is hard to setup. Instructions were misleading. Incorrect information was taught. Accent sometimes hard to understand. Testing did not directly relate to course material and required external study. Tests within instructions videos did not pop up at natural intervals. Overall a disappointing experience.

创建者 Panagiotis P

Apr 18, 2020

The course is definitely one of the worst i had in coursera. Many issues with the sound (week 1) which in combination with the very hard accent of the tutor becomes unbearable for the first 2 weeks at least. The concepts are not explained enough. If you really want to learn choose something else.

创建者 Pietro D

Jan 3, 2020

The course is based on a previous version of IBM Watson platform that makes too many slides outdated. Too much time is dedicated to the definition and computation of basic statistical moments. The same information about Apache Spark is published on the project's website.


Apr 17, 2020

The course has been forcefully put inside the IBM AI Engineering Professional Course, and does not fit in. The course instructor fails to explain the details in an effective way. Overall this course is not designed to be a part of this specific specialization.

创建者 Nils N

Mar 24, 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

创建者 Nazmul H

Dec 20, 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.

创建者 Claudio C

Apr 27, 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!