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

3.9
étoiles
973 évaluations
244 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

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.

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76 - 100 sur 245 Avis pour Scalable Machine Learning on Big Data using Apache Spark

par PARITOSH P

Jan 08, 2020

Good course

par Yassine E

Jan 10, 2020

Awesome :)

par Dr.Lakshmi D

Jul 08, 2020

Excellent

par Krish g

May 30, 2020

fabulous

par shaik m y

May 11, 2020

Good

par ashish k

May 03, 2020

good

par 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.

par 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.

par 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.

par 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

par 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.

par 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).

par 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.

par 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)

par 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.

par 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

par 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.

par 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

par Ali A

Jul 12, 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

Sep 03, 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

Apr 06, 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

Jan 02, 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

Jul 11, 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 Robert v d V

Jul 16, 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

May 20, 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).