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

4.5
stars
3,155 ratings
604 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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76 - 100 of 595 Reviews for Practical Machine Learning

By Keidzh S

Jul 15, 2018

Practical Machine learning helped me to achieve my personal goals. Algorithm of prediction became clear, that gives the understanding of main point of the data science.

By Greg A

Feb 22, 2018

A great course that really helps demystify what machine learning is and how anyone can use it to build prediction models and start to answer tough questions using data.

By Florian

Jul 9, 2016

Great primer for machine learning with ample additional resources for those who are interested. I feel this course gave me a solid basis to delve deeper into the topic.

By Supharerk T

Mar 7, 2016

I want to learn ML in R so I go straight to this course without taking any other course in this specialization, and it doesn't disappoint me. Thanks for a great course!

By Saul L

Feb 8, 2016

This is by far the most enlightening class in the whole specialization. I really got a good handle about how to build a predictive model and apply it to real datasets.

By Camilla J

May 12, 2018

This course was really informative and extremely efficient by letting you know just the few basics needed to build some quite advanced models such as random forest..

By Nikolai A

Mar 30, 2018

This was my favorite class of the specialization. It was taught very well, and I felt like everything I learned in the previous classes were finally coming together.

By Pablo L

Sep 20, 2018

Excelent course, it's a little bit short considering the breadth of the topic, but covers the most important algorithms and never abandon it's focus on methodology.

By Rachit K

Sep 16, 2017

The course gets you deep into ML very quickly ...but I think that's enough to get someone introduced to machine learning. The recommended book a great accompaniment

By Hiran H

Jun 4, 2020

This is by far the best machine learning course I took. This course is more hands-on "Machine Learning" kind rather than providing just a bunch of videos to watch.

By Emanuele M

Nov 15, 2016

It very well done, good pace, and gives you real and concrete elements and examples to build a fully functional machine learning algorithm! i recommend this course

By Piotr K

Oct 23, 2016

Nice introduction to machine learning in R. It is rather basic level, so it not for people that already know some basics related to regression and classification.

By Jan K

Aug 2, 2017

A nice overview of the most popular Machine Learning algorithms. Also very thorough, given the limited amount of time. I recommend anyone interested to take it!

By Francisco J D d S F G

Nov 27, 2016

The best course of the specialization along with the statistical inference one - the final assignment is very fun to do, pretty much like a Kaggle competition.

By David R

Jan 14, 2019

Great introduction to Machine Learning in R. Concepts explained very clearly and project gave opportunity to test out the concepts introduced to real data.

By Vinicio D S

May 22, 2018

You will learn how to use the caret package and learn how to implement ML algorithms. If you want the theory behind it, you need to go to other courses

By Selim J R

Dec 15, 2016

Excellent course. I feel like i know so much already even though we scratched the tip of the iceberg. Will definitely enroll in more advanced courses.

By Pouria T

Jul 2, 2017

Great course, thank you. I was able to use what I have learned from the previous 7 courses and see them on in action through this course. Thank you :)

By Dan S

Feb 25, 2021

The course gave enough of an introduction to allow me to pursue many of the topics on my own. The course does require a fair amount of outside study.

By Sinan G

May 29, 2017

Very fine course in machine learning where the focus is more on the use of ML rather on the theory behind it i.e. the course title fits its contents.

By Deleted A

Dec 1, 2019

The course is amazing. The use of training and testing to predict data analysis made me more fascinated and interested in Data Science. Very nice!

By Jerome S P

Jun 18, 2019

Very good explanation! Trying to do the examples help me understand more plus the explanation which is not on the slide helps a lot. Thank you

By Shashikesh M

Aug 3, 2017

An absolute approach of learning machine learning in very unique practical manner. Fundamental at the same to very practical learning course.

By Chris N

Jun 7, 2017

loved it - fascinating subject and more detail than you could possibly want from the course instructors. Friendly community in the forum too.

By Matthew W

Mar 1, 2016

High level and brief overview but found it informative introduction into machine learning with R. The final project is fun and interesting.