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

4.5
stars
3,107 ratings
589 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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451 - 475 of 579 Reviews for Practical Machine Learning

By Vincent G

Oct 22, 2017

appropriately challenging material.

By PATEL N P

Oct 7, 2016

Nice Course for every New candidate

By Tiziano V

May 25, 2017

Interesting the final assignment.

By Rahul K

Mar 7, 2016

Really Well Structured Course!!

By Robert R

Jul 20, 2016

Just the right level of detail

By Erik K

Jul 8, 2019

Very good. Learned a lot

By Bassey O

May 3, 2016

Very informative course.

By Qian W

Sep 9, 2018

need eva on my project

By Javier R

Oct 14, 2017

Love this class !

By Mehul P

Oct 3, 2017

Good ML overview.

By Lilia K R E

Mar 30, 2016

Muy buen curso :)

By Tiberiu D O

Sep 21, 2017

A good course!

By RAO U D K

Sep 17, 2020

Excellent job

By Raymond M

May 2, 2018

pretty good!

By Piyush P

Jul 13, 2017

good context

By Prahlad S

Jun 18, 2020

great hands

By Timothy V B

Apr 22, 2017

good course

By Rohit K S

Sep 21, 2020

Nice One!!

By Ryan R S

Aug 25, 2020

Very Fun

By KRISHNA R N

Apr 19, 2018

nice

By Sanket P

May 27, 2019

ok

By Yury Z

Feb 3, 2016

I'm somewhat disappointed. I attend almost all other courses in this specialization (except of "data product") and this one is, on my opinion, the weakest one. A lot of links to useful information though. This is more reference guide rather than a real training course.

I can say even more, initially I start other courses of this specialization just because they were marked as strong prerequisite to this one. For now, I think all other courses of the specialization were much more valuable for me than this one.

I've also took Andrew Ng course on Machine Learning in the past, and my learning experience was much better. In lectures on some concepts (like regularization) I'm pretty sure I would not understand anything if I had not been familiar with the subject before..

By June K

Sep 5, 2019

This course does not have the depth it needs, but I do learn a few valuable things. I suggest breaking this course into 2 courses and give more lectures on using caret package and other packages as well. Another thing is I could not ever find the correct answers for the quizzes, and most of the time has to guess and take the quizzes 3 times to get things right.

I invested time and effort in doing the last project; but got a not so good grade due to peer review process. I got every requirement done and even have a direct link to my HTML final report but 2 out of my 4 my peer reviewers have limited knowledge of GitHub could not find my link to HTML file. That said with a higher level courses, peer review process has to be different.

By Francois v W

Dec 10, 2017

The course gives a decent overview of the model building process and covers a good spread of machine learning methodologies. I found that the videos focused too much on some basic/immaterial concepts at times and tended to gloss over the more in-depth or complicated sections. It would have helped if difficult concepts were explained with more examples. This meant that a lot of self study outside the lecture notes had to be done. The way that the final assignment had to be submitted on Github resulted in me spending 8 times longer on learning how to post my results than actually building the model - some more guidance here would have helped a lot as the process was very frustrating.

By Dheeraj A

Jan 17, 2016

I believe this course is critical and much needed given where the Industry is heading. Prof Leek, has tried his best to explain the concepts in a lucid manner, however the complexity of the content, may challenge most students.

A few more examples with R code would have been helpful as translating problem statement to R code may not be intuitive.

I would highly recommend that students should plan to study some advance statistics before attempting this course. Having said that, i think this is a wonderful starter course to get a glimpse of what Machine Learning is all about.