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.
Aug 31, 2017
Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.
por 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.
por Sheila B•
Aug 09, 2018
I've been working my way through the whole track, and this was by far the most complex material--but it was easy to understand because the videos were so clear.
I do have one bone to pick, though: the quiz material relies on very old packages. Again and again I had to finegle something so I could answer a quiz question. That makes you guys look like you are lazily sitting back collecting money but not really doing your job as far as teaching goes. It's time for an update. How hard is it to run your quizzes on updated packages and offer answers that are current?
Aside from that, I find that you explain material very clearly and you are my first choice for picking up a new data science skill.
por Jorge E M O•
Sep 07, 2018
The course rushes over a lot of concepts and it already shows its age - however, it's a pretty solid introduction to machine learning from a practical perspective. It will provide you with a lot of ideas for further investigation and exploration and in the end you'll end up with a wide vision of the machine learning process.
por Grigory S•
Aug 28, 2018
A bit short on practical aspects of different models
por Qian W•
Sep 09, 2018
need eva on my project
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.
por PATEL N P•
Oct 07, 2016
Nice Course for every New candidate
por 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.
por Romain F•
Mar 22, 2017
Good course on the whole, learned a lot and enjoyed it, but it would need to be updated and corrected (certain bits of code don't work as they did when the course was produced, which can be pretty confusing). Would be nice also to add some more content at the end of the course : the lecture about unsupervised prediction felt rushed, and a proper conclusion opening up to the rest of the field would be useful. Anyway thanks again for this wonderful learning opportunity, keep it up ! Cheers
por alon c•
Mar 10, 2016
Great Course, will be nice to have more projects to see how it goes with different data
por Robert O•
Jul 27, 2017
The course subject matter was great but like the course 6 & 7 scenarios i found the lectures didn't reiterate or reinforce key takeaways that are easily confused. For example is cross validation when you split the data into a training and testing, when you have a separate unknown results set to test final training model on. Or does it require doing folds and then breaking each of those up into training and testing chunks. Or why is it not okay to use a model training function that internally does cross validation similar like randomForest documentation suggests. Also things like what the prediction accuracy implies in contrast to the model oob [ in ] sample error estimate and if that estimate is akin to the 1 - prediction accuracy on test data set, i.e. out of sample error estimate. Seems like liitle coverage was given to whether or not there are well known training models to use or if you literally need to try and compare the 1/2 dozen or so common ones out there every time to find out which one to use for a given dataset. Also left confused about overlapping use of words classification model training, i.e. are they synonyms for the machine learning based functions we use to try and fit models to data.
por Carlos C•
Aug 12, 2017
Excellent content so I give 4 starts. I stat less because the trainer speaks too fast.
por Anant S•
Jun 30, 2017
good course for initial understanding of machine learning. SVM can also be included.
por Kevin S•
Mar 03, 2016
Good introduction to machine learning, might suffer a bit from trying to cover too much ground in such a short time.
por Christian W•
Jan 31, 2017
First 3 weeks are manageable and the final project is great! I had a lot of fun :)
por S M H R•
Feb 10, 2016
A good course where you can learn how ML algorithms work practically.
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.
por Lilia K R E•
Mar 30, 2016
Muy buen curso :)
por Lee G•
Sep 22, 2017
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.
por 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.
por Matthew L•
Jan 06, 2016
Really good overview of machine learning techniques and model evaluation.
por 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.
por Nilrey J D C•
Dec 01, 2017
Good introduction to machine learning
por Lukas M•
Oct 06, 2017
The lectures are very good to get the basic knowledge about machine learning. One suggestion is that the lectures can be longer, covering more detailed stuff and a little bit more advanced materials. Moreover, some codes are not explained clean and clear for me. Hope it would be better in the future.
por Raymond M•
May 02, 2018