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学生对 加州大学圣地亚哥分校 提供的 基于大数据的机器学习 的评价和反馈

4.6
1,384 个评分
263 个审阅

课程概述

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...

热门审阅

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.

RC

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.

筛选依据:

201 - 基于大数据的机器学习 的 225 个评论(共 244 个)

创建者 Ramya S

Mar 03, 2018

The entire coursework is very well explained and organized such that we will get better understanding of the terms related to this field. Hands-on exercises have also given better insight of how to use those tools. I would suggest to take this course for getting a brief knowledge about Machine Learning and Big Data.

创建者 Thomas H

Nov 27, 2016

Good overview of working with SPARK and KNIME - acceptable little theoretical background for all the presented concepts for the sake of application use.

创建者 Juan J R M

Jun 27, 2017

It's necessary to get in some themes deeper to understand more

创建者 Vincent R

May 05, 2018

The course was ver

创建者 Irfan S

Nov 25, 2017

Good course for new comers. Regression and Clustering concepts were explained in very high level and need some in depth.

创建者 Teresa S

Jan 30, 2018

Very interesting course that will help us to understand how machine learning course works and also to analyze data.

创建者 Kesanakurthi N

Sep 09, 2018

very good topic learnt from you

创建者 Raul

Aug 27, 2018

105/5000

I lack to have more examples of a complete solution, applying all the concepts in a generic problem.

创建者 Harsh O

Aug 11, 2018

The course was really informative .I got new things to learn.

创建者 Jürgen B

Oct 31, 2018

Reasonable overview. The VM environment is a major challenge for my hardware. Takes more time to make it work than it should. I am wondering if a cloud solution e.g. GCP would be better.

创建者 Gustavo I M

Jul 03, 2019

Good, would be better if was in portuguese. and sometime is very painful configure the machine. But is a good course, better than the previus 3

创建者 Laurent C S

Jul 06, 2019

great course, all examples with Spark and Knime were working for me

创建者 Siva P R

Aug 23, 2019

Good one !!

创建者 HUBER H M

Sep 20, 2019

Thas good course for learn the machine learning and big data

创建者 Verónica Y G Z

Sep 20, 2019

Esta Bien, :)

创建者 Jose R Z

Sep 03, 2019

The concepts in the course are very good but very basics in machine learning, it's a good introduction to knime and spark

创建者 Sun W

Sep 24, 2019

Generally its good content. However the VM setup is still horrible. Many time wasted on debugging and set up environment.

创建者 Jae Y S

Oct 25, 2019

it was very useful for me to get knowledge about Machine Learning with somehow details and reminded me wh

创建者 Harshith

Oct 30, 2019

It was brief and comprehensive , Got to learn various technologies like Knime and Spark

创建者 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.

创建者 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

创建者 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

创建者 Palash V S

Jan 27, 2018

Not hard, a very beginner-level course.

创建者 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.

创建者 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.