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
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.
автор: Matthew C•
Lots of good material, but some things (like PCA) didn't receive enough coverage in the lectures. The quizzes also weren't great at testing the material in the lectures.
автор: Utkarsh Y•
Great course. Only missing piece is the working information / maths behind the models. But as the name suggests it teaches practical approach towards machine learning.
автор: Craig S•
Not as detailed as some others in the specialization which is a shame but good none the less. The videos go through the info quickly so be prepared to go back over.
автор: Roberto G•
Great as an introduction for someone with no practical experience. Lectures are too theoretical and lack some examples to translates the theory into practice
автор: Nicholas T•
Very good course. Fast paced and a lot of self study required to fully understand some of the nuances of the R (if you're not familiar with the language).
автор: Eric L•
Great course, very high paced with a lot of information. would have been great to add two more weeks and another project to use more machine learning
автор: Igor H•
Rather basic, nevertheless a good introduction to the topic of machine learning with R. Mostly concentrated on applications of the R caret package.
автор: Lee G•
A very good starter course on Machine Learning in R with great links to various resources that students and delve deeper into the various topics.
автор: Yashaswi P•
Good Course the covers a lot of practical aspects and relevant to the real world solution.
Good References and Learning Materails are available
автор: Ann B•
Good class to get the basics of Practical Machine Learning. This course is best taken as a part of the data science series from John Hopkins.
автор: Gabriela C V•
It's harder than the previous one. it would be nice to update some the quizzes as they are based on older versions of R Studio libraies.
автор: Hernan S•
The quiz should be constructed in a way that depends less on the version of the libraries used. The rest of course was excellent.
автор: Jakub W•
Vary practical approach, almost no theory or in-depth explanation of the subject, but a lot of focus on applying ML in practice
автор: Md F A•
To me with this course, the best learning aspect is the final project; how to use Machine Learning Algorithms on data analysis.
автор: Rhys T•
Good course, some aspects of the assignment were a bit beyond the scope of what the course teaches but overall I learnt a lot.
автор: Níck F•
Was pretty good, but quite short and some assignments did not align as well with the lecture material as they could have.
автор: Michael O D•
This is a great course, but it would be good to see it updated to use the newer evolution of the caret package, parsnip.
автор: Tongesai K•
Very good course. I am very knew to this topic but am sure will find a lot of application in my speciality - geophysics
автор: Kevin S•
Good introduction to machine learning, might suffer a bit from trying to cover too much ground in such a short time.
автор: SEBASTIAN E C•
Maybe final review must be verified by an expert, also the kind of data to analyse must be change over the time.
автор: Sulan L•
I hope we can have more détails in this cours and to see how to use the algorithms for the big data. Thank you.
автор: A. R C•
I enjoyed it but it needs indeed to deep into many concepts, which are just briefly named during the course.
автор: marcelo G•
Great course, very demanding, but it could use more reading material, ebooks instead of links on video.
автор: Jeffrey E T•
Good overview of available techniques and the Caret package. Will get you started in machine learning.
автор: BIBHUTI B P•
This was a superb module which created a deep learning insight within me focusing on future technology