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.
par Robert W S•
Great intro to machine learning. Several algorithms with some ideas on sampling and pre-processing techniques are covered. Adding a textbook as done with some of the other data science classes would help, but other resources are referenced.
par Sabawoon S•
Excellent course, very practical. Found the project challenging as preprocessing data required some knowledge of the limitation of the RandomForest method i.e. both train and test needs to have same classes of data with similar levels.
par Kalle H•
Nice course that tries to fit a lot of material into four weeks. Due to this, the material is not so deep, although pointers are given to where the student can find additional information related to each subject covered by the course.
par Kamran H•
Pretty good overview of how to build some types of machine learning models through the caret library in R, but not much in terms of the theoretical underpinnings or why one method is better than the other or where it is most suitable.
par Brynjólfur G J•
Some problems with current and old versions of packages and problems with using other packages on different operating systems. Though that did also help foster an independent research style which will help me in the future.
par Chonlatit P•
GREAT course! There are all base of machine learning field. The limitation is blur between basic and detail especially maths. This course, sometimes , show the maths that make you confuse if you're not familiar with them.
par Emily M•
This course gives an overview of a broad subject. My personal feeling is that there could have been some more indepth examples/case studies to demonstrate how to apply these methods and analyse /interpret the outcomes.
par Orest A•
It needs more mathematical detail. Otherwise is a fairly comprehensive class, and a great tutorial on the caret package. I recommend it, if you need to refresh concepts and get some practical exposure to caret.
It is a nice introduction, but the course is not as good as the other ones from the specialization. Nonetheless, it is just right to get into ML, understand key concepts, applications, algorithms and practice.
par Bruce I K•
It's a great course but I hope you add a few things. The course about the machine learning algorithm is so basic. Please get deep into the machine learning algorithm. Then it would become the perfect course.
par Aashay M•
In my opinion this course is highly technical and demanding in nature compared with the others. The learning experience is good and coursera.org has given a opportunity for customization ! thank you Coursera
par Paul K•
Very good summary of strengths/weaknesses of various machine learning algorithms. This lecturer's style and production quality is much higher than in the previous two courses in the specialization series.
par Erika G•
I learned a lot in this class. There are slight gaps from the depth of material covered in the lectures to the quizzes and assignment. If you're good at researching online, you'll be fine.
par haridas P•
This is a well thought about course which focuses on familiarizing the learner on the concepts of Machine Learning and develops a love in the learner towards predictive modeling. Thank you
par Jiarui Q•
It is still kind of hard for a learner to understand the methods. But it gives me a overall introduction of machine learning and I will have further learning in the future.
par 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.
par 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.
par 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.
par 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
par 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).
par 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
par 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.
par 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.
par 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
par 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.