Learner Reviews & Feedback for Practical Machine Learning by Johns Hopkins University
About the Course
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
VG
Nov 9, 2020
Great introduction to ML.Demands focus and hard work. Forces one to review earlier courses - Statistical Inference, regression models, EDA.Leaves lots of appetite for additional knowledge and skills.
JC
Jan 17, 2017
excellent course. Be prepared to learn a lot if you work hard and don't give up if you think it is hard, just continue thinking, and interact with other students and tutors + Google and Stackoverflow!
151 - 175 of 617 Reviews for Practical Machine Learning
By Weiqun T
•Sep 23, 2019
This is a very good basic course for machine learning. I got the basic ideas and skills for it.
By Кирилл С
•Feb 3, 2020
Great course! Very useful to train using advanced classification models and ensemble learning.
By Krishna P
•Jun 20, 2016
Very good content for beginner, lot of learning in machine learning special caret package in R.
By Enrique A M
•Oct 18, 2020
Mil Gracias Maestro Roger y demas docentes, Mil gracias U. John Hopkins, Mil Gracias Coursera.
By Gustavo C G
•Aug 7, 2019
Excellent introduction to machine learning. Great examples and detailed explanations, as usual
By Luis T
•Aug 11, 2022
Amazing course, it covers machine learning basics and popular algorithms and their use cases.
By Thodoris M
•Jul 10, 2018
Practical ML is a great course, that provides training in the practical aspects of the topic.
By Wesley E
•Feb 15, 2016
Great introduction with a broad set of tools and plenty of resources for more in depth study.
By André C L
•Dec 13, 2018
very good practical experience using machine learning models, especially regarding PCA usage
By Raunak S
•Nov 19, 2018
a very good course for those wanting to learn Machine Learning to implement in Data Science.
By Tristan F
•Dec 25, 2019
Lectures were very clear and helpful! Professor Leek was great at breaking down the topics.
By Oleksandr K
•Jul 11, 2018
Great course! However, it would be good to learn about artificial neural networks as well.
By Jean N
•Aug 24, 2017
Very nice Course. I am applying it right away for Predictions in the Telecoms environment.
By תומר א
•Aug 6, 2020
Great course!
Covers basics of machine learning algorithms and how to implement them in R.
By Rizwan M
•Oct 13, 2019
great course. could have explained more techniques in caret package with coding examples
By Connor B
•Sep 24, 2019
Really good exposure to machine learning and builds on the previous course in regression
By Alfonso R R
•Nov 13, 2018
Hands on course. Loved it. It goes a little bit fast, however, the content is ambitious.
By Brian G
•Aug 17, 2017
Great course. Mechanics of the final assignment are more difficult than the work itself.
By Paresh P
•Dec 8, 2020
Explained practical machine learning well, concepts like model stacking really helped!
By Sean D
•Jun 10, 2020
Really liked Dr. Leek's talks, and the subject matter was interesting and kind of fun.
By Konstantin
•Mar 2, 2020
Excellent course. Lots of exorbitantly useful knowledge. I`ve been lucky to start it.
By Donson Y
•Sep 4, 2017
This is a fantasy course to know that how to build your first machine learning model.
By Jorge A
•Apr 13, 2016
I enjoyed a lot this module, I'll use at my daily work some of the features I learned
By Premkumar S
•Mar 16, 2019
Great course and farily challenging exercises! Thank You for putting this together!!
By Sai S
•Jul 17, 2017
Great course. Ways to curb plagiarism & cheating needs to be revisited by your team.