Ratings and Reviews for Applied Machine Learning in Python

good for scikitlearn.

awesome

The course gives a good overview of the concepts and a great paced programming assignments to understand the concepts.

Great course filled with a lot of details. The course does a great job in teaching all the important concepts. I felt the feature engineering should have been a dedicated topic. I got a lot of hints from the discussion forum and surprisingly there are even more concepts you have to learn for building a pipeline, treating categorical and numeric features differently. Overall challenging week4 assignment gives you confidence to deal with real world problem.

Covers most of the basic supervised Machine learning Algorithms in SciKit-Learn from application POV.

Everything builds up very nicely on top of each other. A qualm some might have is that part of the assessments might be very simple. However, this is an applied course and the course material stays true to what it promises.

1- very slow paced lectures 2- very basic and elementary examples To sum up, it is boring and not useful for practical application.

A lot of stuff, compressed in a short time. It's more about memorizing a lot of concepts rather than understanding them. I strongly recommend to take the course of professor Andrew Ng before this one.

It gives a great overview of different machine learning methods. I found useful information that can be missing in other ML courses. Great course!

Quiz material were great!