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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

3.8
1,224 个评分
310 条评论

课程概述

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...

热门审阅

AC
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.

CL
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.

筛选依据:

201 - Scalable Machine Learning on Big Data using Apache Spark 的 225 个评论(共 312 个)

创建者 Diego D

Jul 12, 2020

This course is outdated, and there are a lot of errors in the presentation.

I think most of the videos in the course need to be updated.

创建者 Cristian M

Nov 6, 2020

Quite difficult to understand, since the videos have plenty of errors and the tutor goes really fast with no explanations some times.

创建者 krishna k

Jun 3, 2021

Great course material, but the videos seemed to be confusing and counter productive. The videos are also old and need to be updated.

创建者 Shivam S

Nov 22, 2020

Not enough coding opportunities provided. More Coding assignments and practice will be better and more content is very much needed.

创建者 Sanders L

Nov 19, 2020

Course needs some polishing. Video content seems to be outdated and not delivered in a format consistent with other IBM courses.

创建者 Ratnakar M

Jan 16, 2020

Content was ok , IBM has better course production than this , sorry to say , i m very grateful for the effort

tutor took . Thanks

创建者 TJ G

Jan 11, 2020

This deeply need a much more detailed course on Apache Spark. You need far more than this course to actually get into PySpark.

创建者 Binod M

Aug 11, 2020

Good introduction but seemed rushed and felt like it had lot of gaps . But the explanations that were given were very nice

创建者 Bear B

Jan 22, 2020

Hard to listen video without subtitles.

It be better to show how create a notebook in the watson on the first lecture.

创建者 ARSHAD S A

Jun 27, 2020

It would be nice to have an updated course content video. Other IBM courses are much more updated and interesting.

创建者 Vhui77@gmail.com

Mar 5, 2021

Very hard to understand the instructor. The speech intonation needs to be improved as a first step.

创建者 GUSTAVO E Z

Oct 25, 2020

The english accent of Mr Romeo Kienzler is sometimes difficult to understand but knows the program

创建者 Michael E

Feb 3, 2020

There was not enough learning about how to use ApacheSpark, it was more of a show what it can do.

创建者 Abrar J

May 23, 2020

I think representation should be better and provided coding notebook should be self explanatory.

创建者 Regi M

Jun 15, 2020

The instructor in this course lacks thorough explanation of the topics being discussed.

创建者 Jacobo D L

Oct 18, 2020

would like to have the video examples codes / link to follow the exercises hands on

创建者 Jason A

Feb 4, 2020

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

创建者 Bhaskar N S

Apr 4, 2020

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

创建者 Vitor A

Jun 5, 2020

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

创建者 PRAVIN K R

Jul 21, 2020

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

创建者 Sascha B

Jul 26, 2020

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

创建者 Emanuel N

Jan 29, 2021

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

创建者 Tarun

Jun 1, 2020

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

创建者 Fabio G

Feb 10, 2021

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

创建者 Aaditya M

Jun 26, 2020

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