Chevron Left
Back to Applied Machine Learning in Python

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

4.6
stars
8,514 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:

1526 - 1550 of 1,550 Reviews for Applied Machine Learning in Python

By Oswaldo C

•

Aug 22, 2020

Los videos no son suficientemente extensos ni para explicar el código, ni para explicar la teoría detrás de los algoritmos, se queda a medio camino de los dos siendo insuficiente en ambos casos

By Jean-Michel P

•

Jun 2, 2021

The better course of this stack... and that's all the positive feedback I have. This course is still very poorly designed and unstructured with a bunch of unfixed mistakes after 4+ years.

By Bart S

•

Oct 20, 2021

The videos were presented at a snail's pace, I needed to play them at 1.75 speed. The python notebook assignments were full of bugs and errors which was quite frustrating.

By Eric B

•

Jan 1, 2023

Material hasn't been maintained or updated for years, it's full of errors and broken links. Lectures are very low quality with lots of mistakes and poor quality graphics.

By Vjaceslavs M

•

Apr 4, 2021

This course is outdated by few years and not been updated in general with lots of mistakes in assignments and on slides making it very not ejoyable to use.

By David C

•

Nov 8, 2020

Not as good as prev. courses. Univ. of mic. should update or get ride of this module

By Gallina S

•

Nov 19, 2021

Good curriculumn, nice assignments. Very poorly presented by the professor!!!

By Magid E

•

Mar 26, 2023

I'm sorry to hear that, but if you accidentally delete your assignment, it's important to understand that there is no way to restore it. Unfortunately, even customer service won't be able to assist you in retrieving your lost work.

It's crucial to always keep backups of your work to avoid any potential disasters. Deleting important files can happen to anyone, but it's up to us to take preventative measures to ensure that our work is safe and secure.

In summary, losing an assignment due to accidental deletion can be a frustrating and disappointing experience. However, it's important to learn from this mistake and take the necessary precautions to avoid similar situations in the future.

By Paul C

•

Mar 27, 2021

Frankly the quiz questions are ridiculous and no explanation is given why answers are considered incorrect. The wording of the answers is not clear and any from 5 is 120 permutations. You get three attempts and then you have to wait 8 hours. Not great if you are studying part-time. I gave a star for the quality of the video which seemed good although I already know the theory from my university course. However, there was no written material - which again helps answer the questions. This is only a coursera courses, tests should be there to help learning not hinder it.

By Markus B

•

Apr 15, 2023

While I really appreciated the preceding courses, the topics in this course where poorly explained. The quizzes where not helpful at all and annoying.

Also, there was no support at all in the forum. Particularly when there are errors in the notebooks, I would expect support from the staff.

By Dhawal M

•

Jan 13, 2022

There is no value addition after listening to the video lectures. You might as well just read the suggested Resources and attempt the Assignments on your own. I have never attended college and might assume that all college lectures are drab and monotonous.

By Samyak K

•

Dec 23, 2022

The software doesn't work correctly on the browser and doing some mistakes aren't corrected even in the lecture or the exam itself. I don't know how people completed this course but it wasted my 5 days

By Michael O S

•

Sep 16, 2021

There's a bug in the final homework that the TA and peers don't sufficiently explain how to solve so I can't get the course certificate just by knowing the content taught in the course. It's not fair.

By Osei K J

•

Apr 17, 2023

Very poor learner support. Autograder for assaignment 2 returning error. Not even coursera customer service could offer help but rely on forum which is still not resolve.

By Topiltzin H

•

Mar 22, 2021

Course was not as expected, I think XG Boost for instance is quite large and was covered in less than 20 minutes.

By SAMADRITO B

•

Mar 19, 2021

The course is full of faulty assignment grader and the concepts given are not up to the mark

By Aditya M

•

Jul 17, 2020

Can't the lecturer use proper slides with proper diagrams for a better explanation.

By Deyner E L P

•

May 29, 2022

Demasiados errores a la hora de enviar los laboratorios.

By SHREYAS D

•

Aug 14, 2020

Things in the beginning are not explained properly

By Joe R

•

Mar 31, 2021

Terrible lectures - assignments were good though

By Hamady G

•

Feb 20, 2023

je souhaite me desincrire a cette formation

By Varun D

•

Jul 19, 2022

A lot of the course has much to improve.

By Konark Y

•

May 10, 2020

many issues while submitting assignments

By Oleg G

•

May 16, 2020

enrolled by mistake want to u nenroll

By SHREYANSH A 2

•

Mar 1, 2023

lund course