Ratings and Reviews for Applied Machine Learning in Python

Very interesting and engaging course. I liked graphical comparisons of different models and their params. Module notebooks were very handy while doing assignments. All homeworks were not trivial, developing and demand attention to detail. Big plus for teachers posts at forum - they help a lot while doing quizzes and assignments.

Very interesting and informative! The material outlined in the course, difficult to understand, IMHO, but the organizers and the teacher managed to present it in an accessible form. Special thanks to Kevyn Collins-Thompson for his lectures and Sophie Grenier for her work and attention to the forum.

A really nice course to begin machine learning with

I feel like the assignments for this class were very lacking compared to the other courses in this specialization. They were glorified code copy and pasting and didn't make you learn much. There was much more video instruction than in the other courses in this specialization, though. Definitely would recommend reading the accompanying O'Reily book to help you understand the difficult concepts better.

It was a great course. This course covered a lot of material and Professor explained every concepts very clearly.

Very usefull, easy to understand and full of examples.

Great professor with lot of real world experience.

Perfect and hard course than Andrew Ng's ML course!

My biggest critique of this class is that it is not challenging at all. Homework assignments are just a repeat of the lectures and take less than an hour if you took notes on the lectures. In other words, there is no value in the homework assignments. The first two courses in this specialization were awesome. We did real life examples for homework assignments and through research you learned more than you had asked for. It was perfect. Even in lectures, there is nothing 'applied' about this course. The professor just covers the content with no real-life examples. Very mundane and unexciting. Also, why not talk about multi-label classification? Professor takes a real example with multiple labels (handwritten digits), makes it a binary class and then proceeds to explain it... Thanks. My recommendation would be to restructure the homework assignments. Instead of having 7 questions that spoon-fed you the solution of a primitive problem, ask us to do some Kaggle challenges, or give us a topic that we go out and solve, do some peer-reviewed assignment. Lastly, if you don't have time or don't want to explain important concepts like pipeline, nested cross validation, and multi-label classification, add them as resources. I am NOT confident in my ability to solve machine learning problems in Python from this course, nor is this course worth recommending.

kind of a good course. However, I think too much things have been put into this four-week class. All methods, for example, random forest method need a lot of practice. In the four week, I think I am not familiar with most of these method and I need to practice more in the future.