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Learner Reviews & Feedback for Machine Learning With Big Data by University of California San Diego

4.6
stars
2,151 ratings
451 reviews

About the Course

Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems. At the end of the course, you will be able to: • Design an approach to leverage data using the steps in the machine learning process. • Apply machine learning techniques to explore and prepare data for modeling. • Identify the type of machine learning problem in order to apply the appropriate set of techniques. • Construct models that learn from data using widely available open source tools. • Analyze big data problems using scalable machine learning algorithms on Spark. Software Requirements: Cloudera VM, KNIME, Spark...

Top reviews

PR

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.

BK

Mar 06, 2020

This is starting course for Machine Learning. Very well explained and after finishing this course, one will get interest in continuing and exploring further in Machine Learning field.

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376 - 400 of 434 Reviews for Machine Learning With Big Data

By 19E15A0509 M

Jul 10, 2020

goog cource

By Siva P R

Aug 23, 2019

Good one !!

By Carlos S d l C

Apr 02, 2019

Good course

By SAURAV P

Nov 07, 2016

insightful

By Tu L

May 20, 2018

Too Basic

By Rohit K S

Oct 13, 2020

Nice!

By Fabián S Á M

Sep 30, 2020

Good!

By Hien b L

Jul 19, 2020

GOOD

By Bodempudi N

May 23, 2020

good

By SHREYAS J C

May 18, 2020

Nope

By SELMI A

Apr 14, 2020

good

By Saravanan

Mar 28, 2019

Good

By Praveen k N

May 05, 2017

good

By AGARAOLI A

Feb 10, 2017

-

By 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.

By 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

By 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.

By Sarwar A

Oct 13, 2020

I would like to give a three-star rating because of the following reasons:

1.Very Few Exercises

2.No challenging exercise

3.Only discussed Decision tree classifier

4.There are other important machine learning algorithms.

5.Overall I don't like the design of this course. It could have been degined to prepare learners for the industrial job

By Sebastián C L

Jul 12, 2020

Un curso introductorio a las técnicas de machine learning. Los ejercicios en Knime permiten entender el paso a paso de un proyecto de ML, mientras que los ejercicios en Python son prácticamente replicar el código ofrecido y no agrega valor a menos que conozcas muy bien este lenguaje de programación

By 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.

By Javier P C

Feb 19, 2020

I like this course, but is very old and doesn't have methods for programming like python or other. Please check the content and upgrade the software, for me, it doesn't work Cloudera VM and is very sad. More Quality.

By 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.

By Victor J O

May 10, 2020

The course start excellent talking about categorical predictions but I would like see a similar explanation for regression or numeric predictions. However, the course offer an excellent quality.

By Santiago C F

Oct 05, 2020

The course tries to cover too many areas of Machine Learning, which ends up reducing the amount of time per topic, as well as the information you'll get to see.

By 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.