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返回到 Scalable Machine Learning on Big Data using Apache Spark

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

827 個評分
208 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...



Dec 12, 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.


May 01, 2020

I like the example given and step by step tutorial given. The explanation of why things are the way they are designed certainly helped me understand the concept. Kudos.


151 - Scalable Machine Learning on Big Data using Apache Spark 的 175 個評論(共 209 個)

創建者 Xueling L

Jun 11, 2020

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

創建者 مجید د

May 24, 2020

course video's need a complete revision

創建者 Gao S

Dec 21, 2019

Instructor accent is strong

創建者 NoOneMine

May 12, 2020

Pls improve sound quality


Jun 30, 2020

Difficult to follow

創建者 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.


Jul 08, 2020

Too high-level, mismatch between code and Watson setup Video vs working notebooks, teacher does not explain basic building blocks re RDD and DF what is the difference, when each of them should be used, complicated subjects have videos by 3-5 minutes, absolutely simple exercises. What is the basic difference with sckitlearn and how different work should be organized. NO any supporting materials, some code is not working, errors in videos with clues "don't do this"... Not serious approach for building this course. Sorry

創建者 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.

創建者 Gaby B T

Apr 06, 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.

創建者 Jafar P

Jun 22, 2020

This course is good but could be much better. It is not as clear and explained as the first one "Machine learning with Python". It would be good to keep the same way of explaining. It should be "easy" to understand for someone who has never studied AI before (which is the case for the first course).

創建者 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.

創建者 Pietro D

Jan 03, 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.

創建者 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

創建者 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!

創建者 Juho H

Apr 23, 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.

創建者 Esteban H E

Jan 29, 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

創建者 Friscian V C

Jul 07, 2020

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

創建者 Vikram v s s

Jul 03, 2020

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

創建者 Suman k s

May 17, 2020

Explanation not satisfactory and exercise also not so good.

too much issue in setup all these exercises.

創建者 Billy

Jan 16, 2020

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

confusing to follow

創建者 Tamador A

Jun 03, 2020

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

創建者 Harsh K

Apr 12, 2020

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

創建者 Branly F L

Feb 14, 2020

This course needs more spark towards the student.