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캘리포니아 샌디에고 대학교의 Machine Learning With Big Data 학습자 리뷰 및 피드백

4.6
별점
2,271개의 평가
478개의 리뷰

강좌 소개

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...

최상위 리뷰

JG
2020년 10월 24일

Excellent course. It teaches the basics with a great method and with practical exercises, involving real data. The sctructure is clear and it covers a good amount of topics. Well done San Diego!

PR
2018년 7월 18일

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.

필터링 기준:

Machine Learning With Big Data의 459개 리뷰 중 401~425

교육 기관: siva R

2019년 8월 23일

Good one !!

교육 기관: Carlos S d l C

2019년 4월 2일

Good course

교육 기관: SAURAV P

2016년 11월 7일

insightful

교육 기관: Tu L

2018년 5월 20일

Too Basic

교육 기관: Rohit K S

2020년 10월 13일

Nice!

교육 기관: Fabián S Á M

2020년 9월 30일

Good!

교육 기관: Hien b L

2020년 7월 19일

GOOD

교육 기관: Bodempudi N

2020년 5월 23일

good

교육 기관: SHREYAS J C

2020년 5월 18일

Nope

교육 기관: SELMI A

2020년 4월 14일

good

교육 기관: Saravanan

2019년 3월 28일

Good

교육 기관: Praveen k N

2017년 5월 5일

good

교육 기관: AGARAOLI A

2017년 2월 10일

-

교육 기관: Hendrik B

2018년 2월 21일

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.

교육 기관: Riccardo P

2018년 6월 1일

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

교육 기관: Francisco P J

2017년 8월 2일

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.

교육 기관: Sarwar A

2020년 10월 13일

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

교육 기관: Sebastián C L

2020년 7월 12일

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

교육 기관: Beate S

2017년 11월 16일

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.

교육 기관: Javier P C

2020년 2월 19일

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.

교육 기관: Joren Z

2017년 8월 28일

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.

교육 기관: Victor J O O

2020년 5월 9일

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.

교육 기관: Santiago C F

2020년 10월 5일

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.

교육 기관: Anil B

2019년 1월 21일

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.

교육 기관: Thorsten S

2021년 1월 13일

The course as such is not too bad ... BUT it's nearly impossible to do the hands-on exercises as Cloudera doesn't support virtual machines anymore.