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學生對 IBM 提供的 Scalable Machine Learning on Big Data using Apache Spark 的評價和反饋

1,126 個評分
293 條評論


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...



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.


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.


201 - Scalable Machine Learning on Big Data using Apache Spark 的 225 個評論(共 294 個)

創建者 Jason A


more hands-on would be nice, rather than having so much of the code pre-written

創建者 Bhaskar N S


Compared to other courses in AI Engineering, this one was a bit too technical

創建者 Vitor A


Content was ok. Not many insights why Apache is better/faster than others.



Not Clearly Understandable. Lack of Deep Knowledge provided on the course

創建者 Sascha B


Very high level, exercises could have been more challenging and hands-on

創建者 Emanuel N


Me parecio incompleto el curso. Algunos temas debieron extenderse mas.

創建者 Tarun


Concepts not explained well, have to watch videos twice to understand.

創建者 Fabio G


I would add more practise exercises as well as the intended answers

創建者 Aaditya M


Videos are outdated which makes it hard to follow along sometimes.

創建者 Wenbo Z


The contents are not well-organized and sometimes confusing.



The course is outdated. exemples in old version of spark



Good content but explanations are not always very clear

創建者 Xueling L


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

創建者 Ameya K


Multiple errors in the instructional videos.

創建者 P S


His accent is very difficult to understand.

創建者 مجید د


course video's need a complete revision

創建者 Aditya K


The content is not detailed enough

創建者 Gao S


Instructor accent is strong

創建者 Axel A


Mejorable Course Materials

創建者 NoOneMine


Pls improve sound quality



Difficult to follow

創建者 Hamad


Too Easy...

創建者 Tarun C


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.

創建者 Marc D


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.

創建者 Oakleigh W


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.