I loved this course! It is designed in a way that both beginner and more advanced student can grasp knowledge. New things for me like boosting (ensemble models), decision trees, stochastic gradient descent, online learning (which is not used much by big systems, instead they tend to do something different for incoming new data) and much more are introduced and explained in this course. Recommend 10/10.

This is a great course on ML - Classification that introduces one to the various techniques available in classification and to understand the algorithm under the hood. The course also explains the process, approach for each technique along with the methods to evaluate the results. Overall this takes the student through the next steps of learning classification algorithm from the foundational courses.

I really liked this section on classification. Like with the regression course, complex concepts were explained well with nice examples and assignments. The only issue I had was that some of the coursework can be computing intensive (no surprise there). On the other hand, you really do learn by doing. And, of course, in the real world, computing resources (though plentiful) aren't infinite.

Great course for beginner to intermediate data science enthusiast! This course teaches you how to implement logistic regression, decision tree, AdaBoost algorithm, and stochastic approach from scratch! There's also some assignment to learn how to implement those algorithms in our preferred library. Would be great if Carlos & Emily can bring another advanced machine learning course!

This course is awesome, specially the assignments. In this course, I've implemented most of the famous ML algorithms that our world is now using.

I can't describe how happy I am. Before this course, I looked at machine learning as a difficult field which can't be understood no matter what. Today, I'm capable of doing some great effort.

Thank You so much :)

I think this is an excellent course to give an idea about the machine learning concept of classification. I felt the lectures were to the point, straight forward and more importantly dealt with practical issues and solutions. The assignments are pretty cool, though large amount of code is written at a few points - I still found them pretty engaging.

Amazing Course Module, I learned a lot of concepts for classifications using Decision Trees, Logistic Functions, Boosting, Ensemble and way to attack problems. Also a lot of coding with Graphlab, I personally like to program by my own but I also appreciating the tool for the class and comparing my skills with other tools. Very cool ! Nice Class

A great course! Well presented, does not shy away from the mathematics (very nice optional units that go into more detail for the interested student!), keeps focus on the material and maintains the structure and feel of the specialization as a whole. It's great that we get to actually implement some of the algorithms. Strongly recommended!

A great course. Starting from very simple and easy-to-understand concepts of classification, it takes us through very important grass-root concepts and algorithms necessary not only in classification but in better general machine learning understanding too. Like Precision and Recall, Boosting, Scalability and Online machine learning etc.

The course is very well structured. It starts from the basic classifiers, further moving on to more complex ones. The instructors teach how to implement each mentioned algorithm from scratch, this really makes the course above par.

I loved the course and it helped me to become a good machine learning practitioner.

Thanks Emily and Carlos.

Excellent effort by the tutors to simplify and motivate the learning process (it kept me engaged) One shouldn't forget that this is just a start NOT an end of acquiring the programming skills as it spoon feeds majority of the supportive (or) actual code!! (so please open a blank notebook and write ALL pieces of needed code as well)

I appreciate the way Emily and Carlos explain the concepts. Its very intuitive for beginners and optional sections give further details. The datasets used in programming assignments are taken from real world examples.

Overall an excellent course and really looking forward to completing the series.

Kudos to Carlos, Emily and the team.

I loved this course as I have the previous two courses in this specialty. The instructors, Emily and Carlos, are very special people. They make the course interesting and enjoyable, and they have put together a curriculum that is very well structured and enriching. I consider this learning Machine Learning at its best.

Great course , It gives the idea of how we should do classification from scratch as well as understanding the concept of how to handle large dataset during training. Boosting is one of the most important technique as what I have heard in machine learning and it's great to understand the concept of it.

Very nice feelings from this course. Nice teacher, nice contents and very nice assignements, everything very well structured. As you can see the sentiment coming from my review is a clear +1, so I hope the algorithm looking for good reviews to show to other posible students chooses mine to show up!

Excellent course. Loved getting into the details of classification. This was a bit loaded with couple of quizzes as well as assignments in each module. Some questions were tricky and had to go through the videos again to figure out the correct answer. Carlos explained all the concepts very well

In my opinion, so far the best part in the specialization series. The only thing, that was strange for me is that the effort required for programming varied a lot. So from week to week, it was difficult to predict how much time and effort would be needed to finish the assignments in time.

All the quiz and programming assignments prepared such away that student can easily get into the workflow, concentrating more on concepts without taking much overhead of programming yet need to think rigorously while writing that small portion of "YOUR CODE" parts on couple of occasions

These Machine Learning classes have been fantastic so far, really enjoying them. Very good coverage of topics and challenging exercises to drive home the learning. The effort put into developing the classes has been superb and I look forward to the rest of the specialization.

This course is so good. I can understand the algorithm and know the way how i can apply this for real life. Thanks so much coursera.org and Washinton university made the wonderful job for everybody. After this course i changed vision, innovation and i think people like me.

Great learning experience. Thanks to Carlos and Emily! Loving every bit of this specialization. :)

It would help if there could be a small introduction to other types of classifiers (Naive Bayes, SVM etc), atleast pointing the student to external resources to try them out.

Very informative and understandable course. Teaches the crucial basics behind the machine learning algorithms and introduces many techniques as well. The self learning that comes with the programming assignment tasks also improves one's bug handling skills. Loved it!

Overall a great course and has a very good instructor. Teaches you all the fundamentals behind classification algorithms and models. Contains very good assignments/projects that make you implement the models yourself to get a much better understanding of the concepts.

In detail course for understanding the various concepts of classification. Instead of relying on the libraries, the course focuses on teaching the algorithm implementation using coding language of user's choice. This helps in understanding the algorithms better.

Excellent course except that week 7 th assignment based on ipynb notebook had some redundant questions. Otherwise a good course especially sheds light on Adaboost, ensemble classifiers and stochastic gradient with batch processing.

Thanks Professor Carlos.