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Learner Reviews & Feedback for Applied Machine Learning in Python by University of Michigan

4.6
stars
8,453 ratings

About the Course

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python....

Top reviews

AS

Nov 26, 2020

great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.

FL

Oct 13, 2017

Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!

Filter by:

51 - 75 of 1,539 Reviews for Applied Machine Learning in Python

By Omid

•

Sep 22, 2018

1- very slow paced lectures

2- very basic and elementary examples

To sum up, it is boring and not useful for practical application.

By Sandeep S

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Nov 24, 2019

I am not happy with the course material and the way teachers are teaching.

By Abbas S

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Sep 10, 2020

This is not a good course for beginners.

By kapish s

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May 28, 2019

no teacher intraction

By Vaibhav S

•

Jun 26, 2018

This course provides a brief introduction to many of the vast and dense ML concepts, like Regression, Classification, Clustering, Neural Networks and many more.I took a course by Prof. Andrew ng on Coursera before taking this course. And due to this reason, i was somewhat familiar with the concepts that are being taught in this video.If you are a beginner, i personally recommend you to take Prof. Ng's course on Machine Learning, and then switch to this part of specialisation, by completing the 1st specialisation (2nd is optional but if you are sort of artistic person, and have a habit of visualising things then opt this too). It is best for those who just want a quick recap of some topic.

By Athira C

•

Jan 30, 2019

The course is so informative and interseting.

By Pawan M

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May 4, 2020

This is an excellent course. If you will complete all exercises making sure you complete all questions in each exercise and score almost 100% in each quiz then you will get full value out of course. Deadlines can be reset any time so you can resume courses anytime and you can take your own time as per your schedule. The programming exercises can be solved only when you get the basics right. Else, you will need to revisit the course material.

By Haim S R

•

Jun 27, 2019

Gives practical experience with ML in Python.

Hides the math under the hood :(

However, this course is not enough to become a real data scientist. One needs much more exercises.

By Krishna B S

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Mar 6, 2019

A very comprehensive and hands-on course for learning applied Machine Learning. Many thanks for this course.

By Nick S

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Jan 12, 2024

This is the 8th course I am taking with UoM. The previous experience was great, I really liked it. Unfortunately, this time is different. The grading system is not working. I can't pass the assignments ##2-3. I spent roughly 3 weeks doing this Applied Machine Learning course. Probably, 50% of my time was wasted on attempts to fix the Jupiter Notebook grading system (googling, reading discussion forums, trying to reach out to Coursera). Sadly, the result is negative. If you look at the discussion forum of weeks 2-3, you will see that a lot of students face same problems and receive very limited support, if any. Some of them decided to drop. To clarify, I finished all previous UoM courses with 97-100%. My mark for the 1st week assignment of AML - 100%, final assignment - 100%. Average quiz mark - 90+. 2nd assignment - 0%. 3rd assignment - 0%. System gives no feedback, no comments. Just spits out zeros. No support from the Educator. Obviously, I am not able to complete this course at this point of time and simply have to move forward. I guess, with some new knowledge, that's why 2* not 1*.

By Ankur P

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Mar 30, 2019

Unsupervised learning was missing. The codes written in the lectures were not explained clearly. Some topics looked unimportant.

By Katherine F

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Oct 28, 2020

This is an incredibly dry course from the University of Michigan. In typical academic fashion, it churns out a bunch of lectures, expects you to remember the content, then throws you straight into some quite complicated problems. Half the time, these problems don't even work and you have to dive into the forums to find out how to correct mistakes that the content providers have failed to correct themselves, even several years down the line. There are iPython notebooks you can use to follow along with the lectures, but really they could do with useful information and explanation embedded within them, which is one of the main strengths of iPython notebooks and has been sorely underutilised here. If the course material were presented in a more interactive and engaging manner, the learner might be more motivated and engaged when solving assignment problems. As it is, unless you have prior knowledge or experience within the field, or a mountain load of free time, it's more an education in frustration than machine learning.

By Justin F

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Sep 26, 2017

The quality of this course in the series is a far cry from that of module 1 and 2, which is a shame because this is the one that I was really looking forward to. The professor does not seem comfortable and uses a lot of extra words in his lectures which can make them confusing and rambling. Many questions on the quizzes and assignments are not covered or well explained by the material. Many assignment questions have to be explained by teaching staff on the forums because the task is not clear.

By Martin M

•

Aug 10, 2020

Week 1 was great...and then it all went downhill.

Too much material cramped into 4 weeks. The lectures are monotonous and rarely go in detail and provide real world cases. yeah, the data is from the real world but just punching code without explaining it is not very instructive.

Oh yeah, and lets not forget the last time the course has been updated was in 2017 and none of the bugs that keep popping up with the code and the autograder have been fixed.

By ALONSO A R P D A

•

Jul 11, 2020

Sorry by bad writting, english is my second language, but:

Again, the videos and suggested reads are not sufficient to learn all that is needed in assingments or in real life application. Doing others courses in coursera like courses offered offered by University of Macquaire turn more clear that this course is so hard to learn because there's less things that what is actually the subject

By Gregory O

•

Sep 25, 2017

I was excited going into this course because the others in the series were taught well and I had learned a lot. Unfortunately, this course greatly disappointed. The lectures were dull, included a lot of mistakes, and did not cover most of what was expected during the assignments. All in all, this course was a waste of time versus just learning scikit-learn on your own.

By Shubham N

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Aug 23, 2020

Not happy & satisfied with the assignments. Whenever I tried to submit, always error occurs, mostly files does not exist. Went to forums though, but files are kept elsewhere, especially for Assignment 4. Had to specially download the file and uploaded in the project directory just to work. Need to have proper file arrangements before starting the assignment.

By Nahuel V

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Aug 3, 2020

I am not a big fan of this course. The assignments were too easy up to the last one that was too hard. There is no moderation in the forums, you can ask a question and nobody will answer.

By Subhadeep B

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Aug 20, 2020

The instructor makes me sleepy. The autograder runs outdated versions of many packages and was last updated in 2018. Although the mentors are always active in the discussions forums.

By Thomas M S

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Feb 9, 2018

I do not have the impression after this course that I have reached a level of familiarity that I will continue using the content of this course. Disappointing.

By Dror L

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Nov 25, 2017

great topic, poorly presented. material not well divided among weeks. lots of repetitions. lack of hands on practice until the very last task.

By Kale H

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May 31, 2020

Autograder is poor and professor is hard to listen to. You're better to just do a YouTube tutorial, like Codebasics.

By Rakesh D

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Nov 10, 2019

lectures are boring, not updated but yes i learned something, but its not up to the margin

By Stephen O

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Aug 25, 2020

Desperately in need of an update as much of the code is no longer up to date/broken.

By Keshav B

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Jan 2, 2020

Instructor tell the thing which are far beyond from asignments and quizes