Who is this class for: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.


Created by:  University of Michigan

Basic Info
LevelIntermediate
Language
English
How To PassPass all graded assignments to complete the course.
User Ratings
4.6 stars
Average User Rating 4.6See what learners said
Syllabus

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Coursework
Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

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Creators
University of Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
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Ratings and Reviews
Rated 4.6 out of 5 of 119 ratings

Insightful and hands on course

Excellent course.

This is a great course. Content is highly organized. The amount of lecture material was just about right. The professor is an excellent lecturer. Assignments and quizzes really helped reinforce my learning. If the Autograder is less demanding, this course would have been better in my opinion.

A great introduction to the practical side of machine learning, particularly if you have already taken Andrew Ng's course. It covers a *lot* of material and the pacing is *very* fast. Week 2 is particularly long, and if you are still a student/working it may take an extra week to complete the course. Quizzes and assignments are not terribly difficult, but be careful of the project assignment in Week 4 (though the bar for a 100% is quite low!). Finally, the accompanying Jupyter Notebooks are very helpful and there are many helpful links to outside resources as well.

A few of the lecture videos feel like an early draft rather than production-quality, with lots of time spent on repeating phrases. The instructor mentions things to be covered "later," but that "later" never comes (for example, in discussing Grid Search). For some background, this course appears to have been repeatedly delayed before its release. To me, is understandable that the creators wanted to get this course out given the demand, but the rush is felt.

Ultimately, however, this is still an excellent introduction to Python Machine Learning, and I do feel the course is well worth taking. Just be prepared to do some more individual learning; however, shouldn't one always be for an online class?)