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
6,734 ratings
1,212 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

FL

Oct 14, 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!!

OA

Sep 09, 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

Filter by:

951 - 975 of 1,192 Reviews for Applied Machine Learning in Python

By Alex W

Nov 18, 2019

Lots of minor issues with the Jupyter notebooks that could easily be fixed but the instructors just post a way to solve the problems in the discussion form instead which is frustrating. The material itself was extremely interesting and useful!

By Siddharth S

Jun 11, 2018

It would have been wonderful if the notebook codes were written and explained in the video the same way as in earlier courses in specialisation taking care of the implementation details as well.However still a Good Course of the Specialisation.

By Varada G

Jul 23, 2017

It is a bit dense - be prepared to spend more time working through examples - and reading the reference book. The lectures, unlike the previous ones in this set, does not allow time for you to practice with the examples in jupyter notebook.

By Sparsh B

Jun 08, 2020

This course was really helpful in understanding the working of various machine learning algorithms.

I was able to gain understanding of various evaluation techniques and there usage in different scenarios.

Thank you for this wonderful course

By Mark S

Sep 01, 2020

Lots of useful information, but sometimes the content could have been better explained. Too many errata than necessary in the assignments at the end of each week. I found that the Jupyter notebook would stop working after about an hour.

By Xuening H

Jan 29, 2020

Pro: I really like all the homework. The data is dirty and the work is a little bit challenging but doable.

Con: I prefer more animation in slices during the lectore to keep me concentrated. I get distracted watching the lecture's face.

By Marshall

Dec 18, 2019

I learned a lot about machine learning with python and would definitely recommend for someone with decent python background.. Some of the assignments have some very unnecessary technical hurdles that are unrelated to the material.

By Vinicius G

Nov 20, 2017

Very hard but worth it. I only took one start off because I did not like the professor. Very sleepy voice and not very exciting explanations. Material was excellent and very helpful for the completion of assignments and quizzes.

By Shivam T

May 02, 2020

I completed this course in specialization and this is the only course which is worth of your time, rest two before this course were your head against a wall.

Excellent course with all the understanding a student need.

Thanks :)

By Nicolás S C

Jul 28, 2018

Really good and applied course. It teaches you a lot of powerful tools for machine learning.

The only negative thing is that the week 4 cover hard topics, and the explanations are vagues sometimes, but nothing too terrible.

By Caspar S

May 01, 2020

Very happy with the course content.

On the other hand, certain instances need to be updated/corrected.

For several assignments, the files don't load and you need to dig through the forums.

It would've been 5 stars otherwise.

By Gourav S

Dec 28, 2019

It can be more detailed. It is on broader terms only. I will recommend Andrew Ng ML course to do as well because it covers too many things than this module. Otherwise, this is a good module as well. :) Enjoyed doing it.

By Qitang S

Mar 06, 2019

Good Introduction Courses, but need more guidance for assignments as there is a gap between two of them. Assignments do need some more hours to finish. In all, a great course for anyone to break into machine learning.

By Cat-Tuong N

Oct 03, 2020

Challenging and fun course. The number of topics is on the high side. Maybe break this into 2 courses? The programming assignments are fun. You will need to go to discussion forum to solve often encountered problems.

By VenusW

Jul 31, 2017

Much better than the second course, the materials are carefully prepared and organized, teaching staff are very helpful in solving issues, however, assignments are not so challenging, still needs improvement.

By J W

Jan 29, 2018

Comprehensive and interesting course in Machine Learning. The use of Scikit Learn helps to give a concrete understanding of ML as well as how many specific algorithms can be utilized in real world problems.

By Vishal S

Jun 23, 2018

It's a nice course. It'll familiarize you with different models, evaluation metrics and basics of machine learning and let you practice with some of the real world datasets during assignment.

By Muzahidul A

Jul 07, 2020

assignments were so good. I think there was not enough information given for the quiz tests. And also the code given was not properly explained. But the materials were so good for practice

By Raul M

Apr 28, 2018

A good introduction to algorithms available in python. I didn't give it a five stars because I 'm still confused on which algorithms to pick/use when I want to work on real data problem.

By Julien Z

May 06, 2020

Very good mix of video and python notebook. Some improvement can be done with the AutoGrader like get back the error python stack trace.

Globally, very good course - strongly recommanded

By kai k

Jun 04, 2018

The final assignment passing was a little too east,

there not being need to use fully what I learnt.

Still,the overall course was very good, and I am willing to keep on take other courses.

By Vinicius d A O

Mar 16, 2020

This course was very good, with a lot of information and important tips for me. The instructor is good but he is long winded, so this course was very long with videos during 20 minutes.

By Saman H A

Aug 15, 2019

- more technical materials, comparisons and better classified details should've been provided, especially to be more proportional to the assignments.

-again, subtitles were full of typos

By philippe p

Jun 07, 2017

The course is well balanced but the progression becomes quite agressive at Week3 and culminate at Week4 with a real life case assignment without much guidance. Great experience dough.

By Vaishnavi M

Jun 29, 2020

Amazingly explained. An intermediate Machine Learner would definitely get clarity of concepts already learned and also new concepts explained so skillfully with graphs and diagrams.