Ratings and Reviews for Applied Machine Learning in Python

Gives a good overview on ML-Techniques. I liked the evaluation part. "Applied" means - they provide no technical/mathematical details of the different methods. You should get it somewhere else. Everything is well set up. You need the knowledge of the previous courses of this specialization.

Really good course, although it is more focused on the practical aspect I really learn much more about different machine learning techniques for improving and applying a model.

Well designed course. The last assignment really makes you review every lesson learned in previous modules. I recommend this course for intermediate python developers

Course is well structured, course material also is well defined and learning is excellent. Though Instructor's communication is very laidback. Should have more engagement in tone and connect with enthusiasm.

very deep explanation...super quick guide for beginners interested in exploring ML

Very good explanations. Need full attention. Quizzes and assignments are really challenging. Good learning experience.

there should be some low level usage of sentences for a intermediate programmers,most of times it bounces up the mind ,not able to get the required concept

excellent

Great course, somehow assignments are not always on the same level, the first was easy, the last seemed to be very complex, but was not, the assignment instructions were misleading. Anyway, I enjoyed this course too much and I want now to improve my abilities in underlying theories.

I personally enjoyed this course much more than the previous 2 courses in the specialisation. Overall, this course is ambitious and covers a lot of different algorithms. For each algorithm, a brief intuition is provided and we are taught how to code in Python. For this course, I felt that the assignments were a closer fit to the content covered in the videos (unlike the previous courses where the assignments required much more independent learning). However, this course will not provide the mathematical rigour that some learners may expect. Furthermore, the amount of content covered could be a bit overwhelming. It would be useful if the instructor could summarise the different steps we should take when faced with a ML problem, esp. for deciding which algorithm to use (since so many were covered)