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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

3.8
별점
1,232개의 평가
313개의 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

2020년 3월 25일

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

2019년 12월 11일

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 315개 리뷰 중 226~250

교육 기관: Tarun

2020년 6월 1일

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

교육 기관: Fabio G

2021년 2월 10일

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

교육 기관: Aaditya M

2020년 6월 26일

Videos are outdated which makes it hard to follow along sometimes.

교육 기관: Wenbo Z

2020년 5월 26일

The contents are not well-organized and sometimes confusing.

교육 기관: André S M

2020년 8월 1일

The course is outdated. exemples in old version of spark

교육 기관: THOMONT B

2021년 1월 6일

Good content but explanations are not always very clear

교육 기관: Xueling L

2020년 6월 10일

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

교육 기관: Ameya K

2021년 1월 11일

Multiple errors in the instructional videos.

교육 기관: P S

2020년 11월 14일

His accent is very difficult to understand.

교육 기관: مجید د

2020년 5월 24일

course video's need a complete revision

교육 기관: Aditya K

2020년 8월 4일

The content is not detailed enough

교육 기관: Gao S

2019년 12월 21일

Instructor accent is strong

교육 기관: Axel A

2020년 8월 22일

Mejorable Course Materials

교육 기관: Pawan S

2020년 5월 12일

Pls improve sound quality

교육 기관: Linda A L

2020년 6월 30일

Difficult to follow

교육 기관: Hamad

2020년 9월 26일

Too Easy...

교육 기관: Tarun C

2020년 3월 14일

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.

교육 기관: Marc D

2021년 3월 5일

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.

교육 기관: Oakleigh W

2020년 11월 9일

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.

교육 기관: Cristina G

2020년 4월 14일

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.

교육 기관: Gaby B T

2020년 4월 6일

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.

교육 기관: andrew r

2020년 11월 22일

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.

교육 기관: Panagiotis P

2020년 4월 18일

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.

교육 기관: Pietro D

2020년 1월 3일

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.

교육 기관: ANURAG G

2020년 4월 17일

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.