Ratings and Reviews for Applied Machine Learning in Python

Tough class, learned not to give up and keep trying. Even went back and redid some quizzes in order to get a better grade.

This course is the one that I enjoyed most while learning anything in Coursera. Thank you everyone associated with this course and content.

I think this course would be a bit challenging to someone who is new to machine learning. The professor often glosses over import details and moves a bit quickly through the course material. There needs to be more powerpoint and reading material explain what the videos explain.

N i c e

Good course. Lots of material, and direction what to study. I have really enjoyed it.

Glosses over material (much like prior courses in this specialization), the professor is audibly nervous during recorded lectures, and many assignments require information and functions not covered in the lectures. Additionally, out of date Python modules are used in the notebooks, so you're learning often deprecated usage patterns, not to mention the constant struggle that is the auto-grader. You can teach yourself with free resources and save yourself the money and unhelpful bouts of rage against the auto-grader.

The programming assignments where though because the automatic grader was very picky. Please change it so it gives the user more input about what part of their code is wrong. Also Have a repository where the user can retrieve previous submissions.

Kevyn Collins-Thompson is a legend

This is a great course for those with limited experience of machine learning, wishing to quickly grasp how to apply machine learning methods and get their hands dirty. In my opinion, this is the best course in the specialization so far and as in previous courses you are expected to dig into further theoretical/usage details yourself from online documentation (hence the name applied). Concise lectures and interesting reading materials, as well as hands-on assignments. My recommendation is to either start with this course or take it together with more theoretical courses (such as "Machine Learning" from Stanford or "Machine Learning Fundamentals" from UCSD) to get the full flavour of what machine learning has to offer.

In depth understanding is required to complete the assignments. Challenging without being demanding.