Jul 19, 2018
Excellent course, I learned a lot about machine learning with big data, but most importantly I feel ready to take it into more complex level although I realized there is lots to learn.
Sep 01, 2018
Amazing training on ML for people starting their first experiences with the topic. Practical and easy to understand examples that can be further extended by the student.
por HUBER H M•
Sep 20, 2019
Thas good course for learn the machine learning and big data
por Kartik K•
Nov 23, 2018
The course should cover more topics about Machine Learning.
por Anil B•
Jan 21, 2019
It would have been better if more case studies to work were given. I am surprised that there is no working case study given for regression analysis.
por Palash V S•
Jan 27, 2018
Not hard, a very beginner-level course.
por Alberto T•
Jun 14, 2017
many basic of machine learning but not so specific to big data, only hands-on with pyspark is big-data related
por Riccardo P•
Jun 01, 2018
Not so happy... it would be a little bit better if I attended this one before the ML course by Andrew NG...
Here, the topics are just introduced and poorly demonstrated using Knime and Spark.
Maybe, I had wrong expectations but, given the course title, you need to push more on Spark and leave the ML introduction to better courses like Andrew's one or a dedicated one.
Don't spare too much time with stuff like Course 2 and get some risks
por J. A H P•
Dec 29, 2016
It's ok for an extremely high-level overiew
por Francisco P J•
Aug 02, 2017
Some parts of the course are quite interesting, in concrete, the introduction to the Knime tool (so useful and open source tool which I will try to take a deep look on it as the course only provide a slightly overview). Otherwise, i think that the content is not enough, i don´t feel that I have fully understand the core of Machine Learning and its difference with other BD applications.
por Hendrik B•
Feb 21, 2018
It's better than the other courses of this specialization, but still I wouldn't say that the course is particularly good. Also, the instructors don't appear to care for the learning progress of the learners. There is next to no help via forums, for example. What I think was good is that the instructor attempts to explain the algorithms of the machine learning methods visually and comprehensively.
What I think is a joke is the way the quizzes are organized. The questions almost never deviate from a 'change a number or copy the code' style. Like this, you do not really learn anything instead of copying code and changing something. The quizzes need some additional parts where it is important to apply what is learned to new contexts. ADditionally, the instructors need to put more focus on explaining what certain parts of the code do and why certain parts of the codes are improtant- Otherwise, this course won't be worth more than learning by doing alone.
por Joren Z•
Aug 28, 2017
A bird's-eye-overview introduction of the field. It teaches you some terms and it gives you ideas about which fields might be interesting for you if you want to really learn how to do machine learning with big data.
por Miguel T•
Aug 17, 2018
I miss some technical information about machine learning techniques such as neural networks.
por Ivan S•
Mar 01, 2017
Very basic things... Any examples for regression.
por Artur L•
Oct 27, 2017
Nice knowledge refresher
por PRERNA S•
Mar 15, 2018
It was a basic course for initial understanding about Machine learning.
por Beate S•
Nov 16, 2017
I liked the theory parts, but had a to of problems with the hands on exercises: I spent a tremendous amount of time on installing/trying to install the necessary software. And not everything worked properly on my Mac Laptop.
por Rahul P•
Aug 02, 2019
The Hands-On exercises were good. The theory part was too shallow.
por Michal Š•
Nov 18, 2016
Almost a useless course - ML overview using KNIME which gives no insight whatsoever.
por Csaba P O•
Oct 04, 2017
This course is more "the very basics of machine learning" illustrated with some examples. The lectures were clear and logical, but honestly, very basic. Unfortunately the big data handsons (the ones with pyspark) are not explained very thoroughly, often they just state that "do this or do that" instead of explaining what is going on. All in all, I have expected more big-data related topics and less introduction to machine learning.
por Tobias O F•
Jul 31, 2017
The parts including KNIME was not interesting or educational, it was just an big grind. I feel once you are on a level to use KNIME you know that it is better (and easier) to use other frameworks where you have more control, therefor missing customers the program is meant for.
Additionally the last hands-on felt rushed and just copy-paste to some extent (to being able to complete the tasks), even for me having a lot of jupyter and machine learning background.
por Erik P•
Oct 17, 2017
The virtual machine in this course no longer is functioning. PySpark update seems to not play nice. I think the content also needs some updating for more modern machine learning techniques.. like using big data with deep learning systems like tensor flow or PyTorch.
por Alfonso A G•
Dec 03, 2016
Machine learning is too simplified and spark part is not even explained, also very little relation of all course with Big Data.
por Ruijia W•
Nov 26, 2017
por Manfred K•
Jul 14, 2017
I expected course with more in-depth and more difficult examples, I learned about a few new concepts, most methods were repetitions for me.
por William R•
Nov 20, 2016
This is another course in UCSD's "Big Data" introductory course. The material is not pertinent to a specialty on big data technologies. Further the course does not increase one's knowledge of Machine Learning in any way that justifies spending the time in the course.
por Beatrice C•
Dec 14, 2016
The course content is very poorly explained. The quiz questions don't really test what was taught in the lectures, and the assignments are just copying and pasting things. I feel like I still have a very poor understanding of what was supposedly covered in the course. I cannot generalise or apply the 'learned' information or skills to other topics or researches because I didn't actually understand the core concepts or how to use the programs.