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Machine Learning With Big Data(으)로 돌아가기

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

4.6
별점
2,060개의 평가
434개의 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

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.

필터링 기준:

Machine Learning With Big Data의 415개 리뷰 중 351~375

교육 기관: Manav G

May 10, 2020

More hands on session needed!

교육 기관: Yomin E J M

Jan 25, 2020

congratulations, it's amazing

교육 기관: To P H

Jan 31, 2019

I want more hands on in Spark

교육 기관: Mehul P

Dec 31, 2017

Nice overview to get into it.

교육 기관: Siddharth S

Mar 27, 2019

nice introductory course

교육 기관: ALVARO V M

Mar 16, 2020

Muy bien explicado

교육 기관: Vincent R

May 05, 2018

The course was ver

교육 기관: Gary T

Mar 23, 2020

Good introduction

교육 기관: Juan J R

Apr 20, 2020

Excellent Course

교육 기관: Tejasri C

Nov 23, 2017

great learning

교육 기관: Verónica Y G Z

Sep 20, 2019

Esta Bien, :)

교육 기관: 19E15A0509 M

Jul 10, 2020

goog cource

교육 기관: Siva P R

Aug 23, 2019

Good one !!

교육 기관: Carlos S d l C

Apr 02, 2019

Good course

교육 기관: SAURAV P

Nov 07, 2016

insightful

교육 기관: Tu L

May 20, 2018

Too Basic

교육 기관: Hien b L

Jul 19, 2020

GOOD

교육 기관: Bodempudi N

May 23, 2020

good

교육 기관: SHREYAS J C

May 18, 2020

Nope

교육 기관: SELMI A

Apr 14, 2020

good

교육 기관: Saravanan

Mar 28, 2019

Good

교육 기관: Praveen k N

May 05, 2017

good

교육 기관: AGARAOLI A

Feb 10, 2017

-

교육 기관: 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.

교육 기관: 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