Aug 14, 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
Mar 01, 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.
Jun 03, 2016
This course allows you to implement practical solutions using machine learning algorithms without having to know the mechanisms behind the calculations in detail. Unfortunately questions in the discussion forum were quite rare and many questions were not resolved during this course.
par Swapnil A•
Jun 09, 2017
The course covers few important topics in R like cross validation, decision trees, random forest etc. which comes in very handy for a data science aspirant. It expects the participant to have a descent knowledge in R. Overall, I am pretty satisfied with this course. Thanks!
Oct 25, 2017
This course is brief but it has the 2 best ingredients for having a really decent first step in Machine Learning:
1) It covers a broad group of different algorithms
2) It provides reference material for those in which you want to get deeper.
Really good job in this course.
par Yuriy V•
Mar 10, 2016
I liked the course and found it informative, but wish there were more stuff on unsupervised learning neural network algorithms (SOMs). Learning about most used algos are great, but would also like to know other machine learning algos that are used concurrently.
par Marcus S S•
Feb 25, 2017
Great course! The hands-on approach make it very useful for one to start doing some very interesting analysis in real life! Thanks a lot! You guys could only make some efforts in updating some classes and packages used in quizzes. But the rest was great!
par Rohit P•
Nov 13, 2016
Lectures were not very detailed.
Quizzes were good and challenging, but too many times the results didn't match the answers even when the random seed was set right
Final project should have been more challenging with more models to build and compare
par Subrata S•
Mar 09, 2017
Very good course. The content can be enriched with some more technical details behind the various techniques. There needs to be 1 more course on Practical Machine Learning in the specialization as 1 course is far too less for such a vast topic.
par Samuel Q•
Oct 24, 2018
Good course to get only the basics of machine learning. The assignments and quizzes are great but the lecture material is very brief and short. The references provided throughout the lectures are probably the best source of more information.
par Robert W S•
Nov 22, 2016
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•
Sep 14, 2017
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•
Jun 25, 2018
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•
Feb 18, 2016
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•
Sep 25, 2017
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•
Oct 20, 2018
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•
Mar 12, 2018
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•
Jan 22, 2018
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.
par Bruce I K•
Oct 20, 2016
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 Aashaya M•
May 29, 2016
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•
Apr 08, 2017
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•
Jul 28, 2016
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 Jiarui Q•
Mar 27, 2019
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•
Dec 11, 2017
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•
Nov 17, 2016
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•
Feb 12, 2018
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•
May 21, 2017
Great as an introduction for someone with no practical experience. Lectures are too theoretical and lack some examples to translates the theory into practice