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Scalable Machine Learning on Big Data using Apache Spark(으)로 돌아가기

IBM의 Scalable Machine Learning on Big Data using Apache Spark 학습자 리뷰 및 피드백

3.9
별점
996개의 평가
253개의 리뷰

강좌 소개

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...

최상위 리뷰

AC

Mar 26, 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

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.

필터링 기준:

Scalable Machine Learning on Big Data using Apache Spark의 254개 리뷰 중 76~100

교육 기관: Julien V

Apr 28, 2020

Great course !

교육 기관: Vivek C

Jun 15, 2020

great trainer

교육 기관: harsh s

Oct 17, 2020

great course

교육 기관: Manjot S D

Jun 17, 2020

Masterpiece

교육 기관: PARITOSH P

Jan 08, 2020

Good course

교육 기관: Yassine E

Jan 10, 2020

Awesome :)

교육 기관: Dr.Lakshmi D

Jul 08, 2020

Excellent

교육 기관: Krish g

May 30, 2020

fabulous

교육 기관: shaik m y

May 11, 2020

Good

교육 기관: ashish k

May 03, 2020

good

교육 기관: Aaron C

May 11, 2020

TLDR for those who don't want to read through all of that, the course gives a shallow entry into the data engineering part of machine learning. I wished they would make the course more challenging, so that we would learn more.

For people considering the IBM AI engineering specialization and this course, I would say that it is a very good introduction. For those looking for a more in-depth approach to ML and DL, then this course isn't going to hit those areas. Regarding this course specifically, they did a good job explaining the concepts well. I would have preferred if they made the course proejct a lot less hand holding. They essentially give you the jupyter notebook with all the ETL procedures done, and you change like 4 variables, which isn't really intellectually stimulating or challenging. I understand that the course is meant to be an introduction, but I think asking us to do the ETL by ourselves with less rail guards would teach the students a lot more. Like I would say I learned more about Apache Spark and functional programming from the 2nd module quiz than the course project, because the quiz had us writing the code ourselves, and I had to learn and debug functions on my own.

교육 기관: Simon P

Sep 26, 2020

I can't fault Romeo for his enthusiasm and engagement in the forum, and nor do I think his accent is a problem. I can say I learned something from this course, but there are a few negatives

-- Some parts appear unprofessional. This includes the initial videos filmed in the car, the prompts stating that parts are out of date, and the on-the-fly coding in the week 3 and 4 videos

-- The course is initially jargon heavy, but it is pitched at quite a low level otherwise. There is a lot of hand-holding, for the final project you make two alterations to the code already supplied and then copy and paste the results. It would benefit from a review of the didactics.

-- I would have loved to have had more opportunity to play with the data. Why not a tutorial on using SQL or data cleaning? Why not more on the application of the ML tools? There's a definite feeling of being in a sandpit and not being allowed out.

That said, I now have experience with ApacheSpark and I understand how to use it to implement some ML methods, which is good.

교육 기관: Alpay S D

Apr 13, 2020

The content that is taught was actually satisfying, however, it is obvious most parts of the videos were outdated either due to the fact that they are for another course or they were simply not organized from the beginning. In addition, it would have been awesome If the instructor explained the codes more. I feel that I have learnt the basic idea but I need further self-study to make sense of everything we have covered in terms of the coding.

교육 기관: Pamela W

Apr 15, 2020

I enjoyed this class. I worked with Spark a few years ago, but wasn't aware of Pipelines and Parquet. The incorporation of these concepts into the course was useful. The instructor is engaging, but speaks quickly sometimes and there are some translation challenges with his accent. I found myself reading some of the material because i had trouble understanding what he was saying.

교육 기관: Emmanuel H

Jun 22, 2020

I would like to thank Romeo for teaching me. I apologize to rate the course at 3/5. I did like the course in general but I missed the practice of it. The methodology process did not help me to learn the practice. I scored better in most quizes on the first attempting while I could not guess how the code are written. I wish I did learn to interpret or rewriting the code

Regards

교육 기관: Ravi P B

May 12, 2020

Its a nice course and good way to start Apache Spark.But I feel its a bit too fast as well as too high level for those who are pure machine learner and deep learner practitioners on jupyters and colabs,they are gonna find it bit tough and programming part will go over the head.So Goodluck.

But its a nice way to start learning a fascinating technology of Apache Spark.

교육 기관: Ahmed G

Mar 14, 2020

The material presented in the course is important for everyone looking to go into the Data Science or Machine Learning fields, but some of the examples in the earlier chapters use Python 2 and have not been updated to Python 3. The learner has to go hunting themselves in the forums for official posts on how to fix these error (they were there).

교육 기관: Fabrizio D

Jul 05, 2020

It is a very interesting course. Some videos and lectures however should be improved:

-start with a purpose: what is the goal of this script? What do we want to learn from the dataset?

-the explanation of the sliding windows was a little bit obscure.

The scripts are useful and if the learner plays around with them she/he can learn a lot.

교육 기관: Artak K

Jun 27, 2020

Although this course introduced us to the very important idea: distributed and parallel processing, but I find it too broad and too high level. We didnt go deep into any of the topics, and the assignments are to easy(some of them are already done, you just have to find the correct number for the outputs and place it in quiz section)

교육 기관: bob n

Sep 30, 2020

Interesting, but not much opportunity to practice what is taught. Instructor walks through a lot of examples, but they are hard to follow because his notebook screen is a bit blurry. A lot of type a long, and trust me, or "we will get to this latter". Pretty easy compared to other similar coursera courses I've taken.

교육 기관: SITA R R K

Jul 11, 2020

Found this course difficult compared to others, as i am a mechanical guy. However, resources provided in this course are great. In this course unlike others requires lot of reading from resources. Finally, enjoyed this course. Only thing that troubled me is the instructors slang of English -) which is my problem not his.

교육 기관: Lucas I S

Dec 20, 2019

Like the format of this course, which seems more laid back. Having said that, some of the assignments had some confusing portion, but need to acknowledge this is an intermediate course and not a beginner one. I also missed the part of the explanation that Apache Spark has its own tools vs. using Python's SciKit

교육 기관: Petros L

Oct 15, 2020

Very interesting course, learning about utilizing Apache Spark parallel processing and how to build ML models. Video quality was not satisfactory for viewing the described Python code and I had difficulties understanding the spoken language, fortunately the video's transcription helped.

교육 기관: Avashen P

Apr 05, 2020

Great course. There should probably be more coding tests where submissions get you a grade like some of the other Coursera coding courses.

Some of the coding in the lectures is a bit too quick, but that's probably just for because I have never used the Apache Spark syntax before.

교육 기관: Dhaivat H P

Apr 21, 2020

Very good teaching techniques, The professor explained everything well, The sound quality was dull on 2nd week's video and the accent was a bit tricky for me but the quizzes were good and if you code with him you'll be able to understand the concepts easily