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Avis et commentaires pour d'étudiants pour Scalable Machine Learning on Big Data using Apache Spark par IBM

3.8
étoiles
1,119 évaluations
291 avis

À propos du cours

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...

Meilleurs avis

AC
25 mars 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
11 déc. 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.

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101 - 125 sur 291 Avis pour Scalable Machine Learning on Big Data using Apache Spark

par Brice S

5 janv. 2021

I really enjoyed this course, I think despite this it requires a review to make it more consistent. One thing that would have made it better for me. Would have been to have the jupyter notebook matching exactly the video so I could have worked on them in parallel... Thanks very well build course, it really gives a good base to start using Apache-Spark ML

par Ahmed G

14 mars 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).

par Fabrizio D

5 juil. 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.

par Artak K

27 juin 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)

par bob n

30 sept. 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.

par SITA R R K

11 juil. 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.

par Lucas I S

19 déc. 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

par 이지양

30 oct. 2020

Sources in the lectures were really great to understand what is Apache Spark and How to use it.

However, in some part of the lecture, I loss my way to understand what's going on here...

Anyway, at final course, I could review what I learned in this course and that will be a good guide to use Apache Spark.

par LEE L H

26 déc. 2020

Slides contain some typo in Python codes but highlighters are available to let you know what are wrong. However it still makes me feel that the course materials are not very well prepared.

Good thing is thing I have got a basic understanding about how Apache Spark can facilitate machine learning.

par Petros L

15 oct. 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.

par Avashen P

5 avr. 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.

par Dhaivat P

21 avr. 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

par Ali A

12 juil. 2020

I like the course, but it fails to mention clearly how learning apache spark could help us. Also, it requires a certain amount of coding experience, I was able to finish it, but sometimes I had no idea what I was doing.

par Rich P

3 sept. 2020

It was surprisingly fast-paced. There were a few intuitive leaps, including a bad data reference on the final project, that were potential stumbling blocks, but I feel more confident having overcome them.

par Sourab M

6 avr. 2020

It is a good course for beginners in the domain of Apache Spark and Apache Spark ML. Programming assignments could have been better if they were applied to "Big Data" and not on toy datasets.

par Miele W

2 janv. 2020

Again a nice course that introduce you on Apache Spark Usage. Just a little suggestion, if you could insert a little tweak on how pass from spark to pandas and vice versa.

Enjoy :)

par Dhivyarupini R

11 juil. 2020

Teaching was clear and understandable. Only feedback would be I hope the lab work would be more hands on because I'm worried I don't pick up the concepts unless I type them out.

par Ihsandi D

23 janv. 2021

Depending on the student, this can either be an easy or a difficult course. Some parts needs update, and it would be great if there are more explanation on the algorithms.

par Robert v d V

16 juil. 2020

Nice introduction to Big Data processing, No coding skill required. A little more focus on the theory would be nice as the Python coding exercises are a little redundant.

par Giorgio G

20 mai 2020

Great tutorial overall.

Room for improvement: Fix the differences int the definition of kurtosis and skew between vide, test, examples (preferable the scipy definition).

par Zaheer U R

1 juin 2020

It was a very interesting and skillful course. Thanks to IBM and Coursera for such a wonderful course. Special thanks to Mr. Romeo Kienzer for explaining it so well.

par leonardo d

24 févr. 2020

There are some issues with the normalization of the distribution moments. Everything else is good material to learn how to use apache-spark for the first time.

par Julien P

9 juin 2020

Great notebooks. But the videos are getting old and are a bit obsolete compared to the contents in notebooks. I would have also appreciated more theory.

par Chokdee S

4 mai 2020

Learning material is pretty good for getting started with Apache SparkML. Everyone who leaps into Scalable Machine Learning this is one of your choice

par Brandon S C

18 févr. 2020

I found this course incredibly beneficial. Moving forward, I would like to see a bit more explanation of concepts and few extra workable examples.