Chevron Left
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
847개의 평가
210개의 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

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.

M

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.

필터링 기준:

Scalable Machine Learning on Big Data using Apache Spark의 211개 리뷰 중 51~75

교육 기관: Андрей К

Feb 08, 2020

great and easy pyspark introduction and implementation

교육 기관: Ahmet

Dec 21, 2019

There should be more practice notebooks and questions

교육 기관: MD I A

May 01, 2020

Excellent courses much interesting statistical part.

교육 기관: Darnesha C

Jan 29, 2020

I really enjoyed this course! it made learning fun!

교육 기관: Carlos F C d S e S

Feb 12, 2020

This course amplify our vision about Big Data!

교육 기관: Mathews J G

Jun 30, 2020

quite a good course for data science interest

교육 기관: Fahad T A

Feb 29, 2020

It was challenging and very informative

교육 기관: Nelson C S

May 28, 2020

Excelente Curso, muy bien explicado !

교육 기관: Manjiri N

May 31, 2020

I find this course very interesting.

교육 기관: Nguyen T T

Jun 09, 2020

Handful material, great course!!!

교육 기관: Pratik P

Jun 14, 2020

Great Course Highly Recommended

교육 기관: Michel G E H

Mar 23, 2020

Amazing course! Thank you!

교육 기관: Krishna H

Apr 26, 2020

Very good course!

교육 기관: Ever A B V

Mar 26, 2020

excellent course

교육 기관: SAMIR B

May 09, 2020

detailed course

교육 기관: Julien V

Apr 28, 2020

Great course !

교육 기관: Vivek C

Jun 15, 2020

great trainer

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: 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.