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

4.6
stars
7,130 ratings
1,295 reviews

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.

OA
Sep 8, 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses

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26 - 50 of 1,273 Reviews for Applied Machine Learning in Python

By Amir A C

Jan 19, 2020

Unfortunately, for me, this course (not the specialization) seems to be a "review of" Applied Machine Learning in Python" rather than "teaching" Applied Machine Learning in Python. Some codes used in the notebook were skipped by the instructor.

By Mahmoud

Dec 28, 2018

Week three is the worst ..

Lecturer is getting confused a lot in an already confusing topic which ofc makes me resort to outside readings in order to grasp it and leading to stretching the time I need to finish this week

By Gregory B

Jun 14, 2017

I'm disappointed that I took this class, poor design and delivery. Machine Learning is an exciting and fun topic, but you'd never guess it from this class, and the way the instructor delivers the content. It's a shame that the designers want to throw every possible model at you in 1 or 2 weeks, before having a discussion on model evaluation. This course focuses more on the academic than the practical, and doesn't try to explain these topics in an approachable manner. There are far better and engaging options available.

By Saqibur R

May 3, 2020

This course is all over the place, and compared to the previous courses in this specialization, this seems like more of an effort to gloss over the documentation and capabilities of SciKit Learn rather than focusing on a handful of the most important ones. The course lacks focus, the material taught is not rich, and you are better off just reading the documentation on your own. The book recommended at the start of the course is excellent, and reading that instead might be more fruitful for you.

By Rishi R

Jul 6, 2018

Rather then writing code while explaining like the intro and plotting in python, the instructor shows it like slides, its hard to follow which chunk of jupyter notebook he is explaining, and requires lot of back and forth to read the code. Very bad way of explaining the codes.

By Sean D

Jun 12, 2019

This is the worst course in the specialization. The autograder is bad. There is inadequate explanation about when to use the different models. Presumes way too much about the student's level of knowledge. Would not recommend.

By Craig A B

Nov 2, 2018

There's too much back to back to back video lecture and not enough hands on work. The final quizzes and projects are too challenging given the amount of work done on the subject matter.

By Sudhir J

Feb 17, 2020

Very poor configurations. I am tired of submitting assignments on auto grader. This is the first time I am having such terrible experience with Coursera. Hope you improve.

By Ipsita D

Apr 20, 2019

No visible support from groups forum. Videos knowledge is limited to complete assignment or quiz.

By Shaoqi C

Mar 10, 2020

This is my worst experience of submitting assignment and I found out that I'm not alone

By Amber S

Jul 21, 2020

The guide could not explain the concepts well. He was just reading from the slides.

By Pei L

Jun 27, 2020

Bad teaching, unclear explanations.

I learned half of the material from Youtube.

By Nomthandazo T

Mar 2, 2020

this is the worst course ever. so bored and frustrated

By Edward G

Jan 24, 2018

Terrible quiz problems and grading mechanism

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 Pankajkumar S

Jun 4, 2019

This is an excellent course. The programming exercises can be solved only when you get the basics right. Else, you will need to revisit the course material. Also, the forums are pretty interactive.

By Athira C

Jan 30, 2019

The course is so informative and interseting.

By Pawan M

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

Mar 6, 2019

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

By Shiomar S C

Oct 14, 2019

Honestly this course was somehow disappointed I really wanted to learn a lot but the professor was somehow discouraging, he repeated himself a lot, and for an online course and every video been 20+ minutes long and at the end only been useful 4 or 5 min of it… having so much errors during lecture and not following the notebook as it was given to us make it more difficult to learn… I’m choosing this platform (and paying) due the professor been good and this one make learning more difficult than the previous one.

By Sajjad K

Jul 13, 2020

Teachers are very mediocre. They make way too many mistakes. Their pronunciation is stoic and muffled at times - makes it hard to follow.

By fulvio c

Feb 25, 2020

The video and training provided it's not providing enough information in order to complete the assignments.

By Rakesh D

Nov 10, 2019

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

By Robert S

Jun 11, 2020

I had high hopes going into this course after the really well put together courses 1 and 2 in the specialisation, however the video material was dull and disengaging. Where the lecturer could have spend hours going into the ins and outs of how the different algorithms work, instead the course followed a structure of: 1 - Brief overview of an algorithm, 2 - whats the syntax in scikit-learn, 3 - what parameters does it take, 4 - what other commands are there

I was really disappointed, as most of the actual learning was done from reading other sources on the web and watching videos for free on YouTube. I guess the only positive is that because I paid for it I was forced to finish it?