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Learner Reviews & Feedback for Practical Machine Learning by Johns Hopkins University

4.5
stars
3,147 ratings
601 reviews

About the Course

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation....

Top reviews

MR
Aug 13, 2020

recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course

AD
Feb 28, 2017

Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.

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501 - 525 of 592 Reviews for Practical Machine Learning

By Fernando M

Feb 3, 2016

Class materials and videos are confusing and do not go into enough detail. Assignments require a lot of search of extra information outside course materials. Also, the length that is needed to complete the assignments vary widely week to week.

By Eric S

Jul 5, 2020

Weakest class in the Data Science Specialization so far. Don't expect to leave with a deep understanding of the machine learning techniques covered in this course. You will get practice using the caret package in R, which is very useful.

By Ada

Nov 14, 2016

Although again very interesting, I found the lack of additional materials such as practical exercises, swirls and a book reduced the depth of the course knowledge for me. Maybe we have been spoiled by the previous courses :-)

By Ivana L

Feb 24, 2016

Compared to previous two courses in specialization this one is far worse - it is more of excursion into used methods than actual learning using any of mentioned methods in enough detail to be able to do meaningful analyses.

By CHEN X

Dec 2, 2015

Feels like everything is solved using a caret package, while the back-end theory is only slightly touched. By using a single line command solver, student may lack the foundation for harder problems in the real world.

By Daniel J R

Jan 17, 2019

Seems like a lot to pack into 4 -weeks. Should really be named introductory machine learning. Needs more depth and better development of the intuitions associated to each algorithm class to match the expectations.

By Ayushmaan D V

Aug 16, 2020

The material covered was good and informative, the reference material was nice. But the video leactures themselves were lacking in many respects. The videos covered only a bare minimum and could have been longer.

By Vinay K S

Feb 19, 2017

I like initial courses like Exploratory Data Analysis but later on it got harder to follow the lectures. A lot of topics were just rushed through and little effort was made to make them engaging or interesting.

By Andrew W

Mar 13, 2018

Very interesting subject area, I think there is simply too much to cram into one course. Should consider spliting the subject into 2 courese or simply concentrate on only 1 or 2 main areas (e.g. cla

By Andrew W

Feb 9, 2017

The videos are really tutorials on R functions for machine learning and data wrangling. A good substitute for "Machine Learning" by Andrew Ng in terms of managing data sets and exploratory analysis.

By M. D

Jul 11, 2020

Content somewhat outdated. Referenced packages don't always work in current version of R. Material can be better explained with more detailed discussion of examples rather than theory.

By Robert C

Aug 1, 2017

This course needs swirl assignments. I did fine on the quizzes and assignments, but I only feel like I learned a minimal amount of machine learning, even practical machine learning.

By Raul M

Feb 12, 2019

The class is good but it is too simple. I expected the professor will provide more detail about the models. This is just an introduction and weak for a specialization.

By Brian F

Aug 15, 2017

There was some good material in here, but it was rushed and is deserving of a much longer course - especially compared to some of the other modules in this course.

By Chuxing C

Feb 5, 2016

the lack of assisted practices made it harder to digest the contents and methodologies.

strongly suggest to develop some practice problems with explanations.

By Michalis F

May 26, 2017

Good in introducing caret package and getting some experience in running algorithms. Was expecting more in-depth discussion about the methods though.

By Davin G

Aug 26, 2019

It's an excellent crash course to machine learning but the stats part was rushed. Had to look up external resources to understand what was going on.

By Léa F

Jan 9, 2018

Rather good overview. The contents could dig deeper into each subject, and it would improve the course a lot if some exercises in Swirl were added.

By Miguel J d S P

May 19, 2017

I didn't enjoy the supporting materials and the quizzes weren't very interesting. The final project was fine.

The subject is super interesting.

By Max M

Dec 12, 2017

Should have gone into more depth and included swirl lessons, like previous courses. The quizzes were very challenging though, so that helped.

By Kyle H

May 9, 2018

A brisk introduction to some of the basics of Machine Learning. Will leave with an understanding of a few ways to use the caret package.

By Manuel E

Aug 8, 2019

Good course, but either explanations are too fast paced for the level of difficulty, or my neurons have began to decay with age.

By Noelia O F

Jul 19, 2016

Good course for learning the basics of the caret package. However, it is not a good course for learning machine learning.

By Joseph I

Feb 1, 2020

Material was very interesting but was covered at a very high level and a lot of additional learning was required.

By José A G R

Feb 5, 2017

Superfluous but the existence of the package "caret" covers the gap of other libraries like "skilearn" of python