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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
1,211 évaluations
307 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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226 - 250 sur 307 Avis pour Scalable Machine Learning on Big Data using Apache Spark

par Ameya K

11 janv. 2021

Multiple errors in the instructional videos.

par P S

14 nov. 2020

His accent is very difficult to understand.

par مجید د

24 mai 2020

course video's need a complete revision

par Aditya K

4 août 2020

The content is not detailed enough

par Gao S

21 déc. 2019

Instructor accent is strong

par Axel A

22 août 2020

Mejorable Course Materials

par NoOneMine

12 mai 2020

Pls improve sound quality

par Linda A L (

30 juin 2020

Difficult to follow

par Hamad

26 sept. 2020

Too Easy...

par Tarun C

14 mars 2020

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.

par Marc D

5 mars 2021

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.

par Oakleigh W

9 nov. 2020

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.

par Cristina G

14 avr. 2020

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.

par Gaby B T

6 avr. 2020

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.

par andrew r

22 nov. 2020

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.

par Panagiotis P

18 avr. 2020

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.

par Pietro D

3 janv. 2020

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.

par ANURAG G

17 avr. 2020

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.

par Nils N

24 mars 2020

Maybe I do not have knowledge about Python, but a lot of things were not understandable for me. In addition, parts of the course are still shown in an older, out-of-fashion version of Watson. The shown code is not working in todays version

par Nazmul H

20 déc. 2020

This course and assessment method is not standard enough. It should have more practical exercises such as RDD programming, ML, SQL. Course project should have related to developing mini big data application with RDD to ML prediction.

par Claudio C

27 avr. 2020

The course should be reorganized. The video are taken from different courses and is not fluid follow it. There is very little in programming with functional programming. There are many concepts but not well explained. Not advided!

par Juho H

23 avr. 2020

The course teaches important concepts and skills on how to scale your machine learning algorithms - but it is in a desperate need of an overhaul to fix the numerous errors in the videos and workbooks.

par Camilo F P

9 sept. 2020

Did not like it that much, I´m not clear about spark and big data use cases for ML. The themes were variated and did not follow a line or path of learning.

par Esteban H E

28 janv. 2020

Not clear at all.

A lot of things are not explained, or explained in a confusing way. I learn more by researching what things meant than from lectures

par Friscian V C

7 juil. 2020

I dont like the instructor very much. I feel like his explanations are not the best and everything was just too fast.