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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!!

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126 - 150 of 1,539 Reviews for Applied Machine Learning in Python

By Li Z

•

Oct 23, 2017

The course is very well-designed, with the first three weeks learning basic know-hows of all the tools we need, and the fourth week make full use of every model we've learned.

Even people with no prior CS background can get along well enough.

Getting 100/100 out of the final problem is actually a passing grade, very easy if you use what you've learned so far the right way.

When you're willing to spend more time exploring the models, methods and parameters, the reward will be worth your efforts.

By Refik E

•

Sep 20, 2017

I thank Dr. Kevyn Collins-Thompson and Coursera team for the excellent course. I have learned valuable skills from the course. Dr. Thompson explained ML concepts very skillfully and made the course fun to follow. Assignments are very well selected and reinforce the class concepts. Over-all the course encourages learner to investigate and apply different ways to do same task. I recommend this course to those who are willing to learn machine learning and can't decide where to start.

By Tony K

•

Jun 5, 2020

A solid course. The help found in the forums was also way more useful than the first course in this series. While course two was generically useful, this third course was technically useful. A very good introduction into sklearn. The video instructor/professor was also very clear and methodical in presentation. The assistance by the class monitors was leaps and bounds more useful in this course than course one (I almost quit after course one because of it, so glad I didn't!)

By krishna c

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Jul 4, 2017

excellent course for following reasons:

1. Excellent i python note books. What ever a student must know is kept in it.

2. every topic is explained simply and well upto what ever we need to know.

3. if you are not in academic field(not planning to do phd on this stuff). Trust me how ever advanced courses you do but after a week or month. these are the points which one need to remember.

4. Course and programming labs are in perfect sync.

Thank you very much for keeping this course

By SHAILESH K

•

Oct 21, 2019

Great intro course to Machine Learning. Gives you a good overview of the main models and Python needed to code. I liked the fact that it did not get too detailed into the Math foundations of ML. There are other courses for that.

I can apply what I have learnt right away on my job.

Highly recommend.

One Note: this course is over 2 years old and the Staff is pretty slow to respond. But the Forums have enough information to get you to self-solve your problem.

Good luck.

By Ginger d R

•

Apr 21, 2023

Just finished a bootcamp and this ML course is on par with the ML assignments we had to solve. Lectures are great and by far the best course in this specialization so far.

What's scary is that I tried the first three assignments after I completed them using code written by chatgpt and it passes about 60% of the tests. Guess in a year or so DS and ML will all be replaced too:) I wonder if it can also do kubernetes & docker because if it does, no ML job is safe:))))

By Kedar J

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Sep 25, 2018

Great course filled with a lot of details. The course does a great job in teaching all the important concepts. I felt the feature engineering should have been a dedicated topic. I got a lot of hints from the discussion forum and surprisingly there are even more concepts you have to learn for building a pipeline, treating categorical and numeric features differently. Overall challenging week4 assignment gives you confidence to deal with real world problem.

By Mario H

•

Oct 26, 2020

I have done several of Coursera Courses and also from Udacity (Deep Learning Nanodegree) and I find the courses from the University of Michigan really good. This one for Machine Learning is really specialized for the Application of Machine Learning Algorithms. Sometimes a little too superficial, but it is enough for start working with Machine Learning. The Test at the end of the week are a little difficult but you learn alot from them :-)

By Mohammad M T

•

Jul 25, 2020

I think there were some small problems in the assignments and quizes but all in all those problems made this course assignments even more powerful because it demanded more effort to answer those questions properly.

Totally if you want to get a good sense of machine learning and step into AI , this course will not only give you basics and principals but also you will be able to build and understand different models using python.

good luck!

By Erick S G P

•

Dec 7, 2020

All the exercises were very challenging and allowed me to apply all the knowledge acquired during the lectures and even more. I loved the fact one has to search for extra information for doing the exercises, because that pushes me forward to learn to search in other sources. Also loved the freedom that there is when solving the final assignment. That is the best expression of a real world challenge and allowed to exploit my creativity.

By Illia K

•

Dec 18, 2017

This course gave me some tools to use in real life. It's pretty abridged in time because they are trying to cover a very big topic in only 4 weeks. It won't give you a comprehensive set of knowleadges, but a good basis to proceed by yourself. Also some basic knowledges are reqired in computational mathematics, statistics and programming for applying this course. I highly recommend this course as a first step into machine learning.

By Konstantinos V

•

Jul 4, 2022

A good start for someone who wants to learn machine learning in python, for example the commands etc. As for the overall machine learning in theory, I does not recommend this course because everything is explained very briefly. For me it was a great experience to finally learn to use python not only in machine learning but in data processing. I really enjoy the final assignment because I love exploring and processing the data.

By amr7684

•

Sep 2, 2018

One of the best ML courses on the platform. I highly recommend it to all data-science enthusiasts. It would be nice to have pandas data-wrangling skills before tackling the final project as it is a must. Totally enjoyed the final project! was a great learning experience seeing my classifier AUC going from 57 all the way to more than 76 and the impact of feature importance and cleaning on the model performance was eye-opener!

By Michael T B

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Dec 19, 2018

Great class! I had fun learning many new things in this course. The professor did a very good job at taking a complex subject and making it simple and easy to understand. The code and assignments were straightforward and not overly difficult. The real quizzes/tests in this course were appreciated as this felt more like a "real class" where one can really learn a lot. One of the best online classes that I have taken.

By Parvathy S

•

May 13, 2018

Very useful and true to the name, it teaches Applied Machine Learning - how and when to carry out the various algorithms on a dataset, how to tweak the parameters and tune the model. Really Really helpful if you're looking to finally get your hands dirty on data after reading all that theory!

Also gives brief but necessary summary to all the different algorithms with intro to deep learning as well. Highly recommended!

By Benjamin S

•

Oct 26, 2017

I thought this was a very good course in Machine Learning using Python. I took Andrew Ng's Machine Learning course before this one, which I would highly recommend! I enjoyed this course because it taught me about scikit-learn, which I plan to use in my career. I also purchased the recommended textbook "Introduction to Machine Learning with Python" from O'Reilly, which I found to be a very useful reference.

By Fabio C

•

Jun 22, 2017

The course is well done and both the lectures and the practical assignments have generally a high quality. If you come from a theoretical background, be aware that this is a very "high level" course, meaning that a lot of attention is put on the practical application of the different ML methods (using the sci-kit learn library in python), but very little is said about their mathematical foundations.

By Zhuohan X

•

Nov 4, 2019

All complicated math acknowledges were cut off and fully focused on applying ML using python. As an energy engineering master student who doesn't have much programming experience, I find this course very useful. PS. I've previously taken the specialization 'Python for Everybody' to get familiar with python. I suggest doing the same if you also have no idea of python just like I did when I started.

By Perry R

•

Jun 30, 2017

Excellent instruction and challenging assignments! Sophie from the teaching staff was very helpful and responsive to forum posts. Thanks to Kevyn Collins-Thompson for a great survey course in machine learning. The only downside was that the auto grader has limitations which inhibited some exploration (one can not keep plots in the submission is an example), but I'm sure that will get worked out.

By Fabiano B

•

Mar 8, 2019

The course is a great overview of the basic algorithms that every machine learning practitioner should know. Since it has a limited amount weeks to cover such a broad subject, you will have to dig a little deeper by yourself. I found the reading material also very interesting. The final project is awesome and it will definitely make you experiment what is exactly what a Data Scientist should do.

By Ling G

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Aug 17, 2017

This is a great course I learned a lot, especially it familiarize me with the SKlearn toolkit which is very very handy. I notice that the SKlearn documentation contains a good figure which shows a rule of thumb which learner to use. I recommend you to include in course reading, because some students might find it very useful.

http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html

By Nattamon T

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Jun 21, 2022

The course does a job on giving an overview of how each machine learning algorithm works without having to go deep into details. Instead of mathematical destials, the course places emaphasis on how machine learning is applied to solve problems and some practical consideration. I took machine learning courses during my master but I found this course a great complement on what I have learned.

By Alan J

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Jul 2, 2017

This was an awesome and engaging course. Machine Learning is a vast field with lots of ground to cover. This course gives a broad overview of all the different parts of machine learning without going too deep and also keeping everyone engaged. The assignments, especially the last one test what you learned and keeps you on your toes. A good beginner course to Machine Learning. Thank You!

By Lawrence O

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Jun 29, 2017

Very informative about machine learning approaches ie supervised and unsupervised learning. And then goes into detail about the techniques such as regression and classification for supervised learning and clustering (K-Means) for unsupervised learning. Other techniques are discussed such as Principal Component Analysis etc.

I enjoyed it and would recommend for all data enthusiast.

By Peter B

•

Jul 11, 2018

Kevyn is an absolute joy to learn from. His enthusiasm for the topic is contagious, and his explanations are clear. The course content is well curated, tested, and reinforced. At the end of this course I feel confident that I can *actually* apply machine learning to real world problems and competitions. This is not just a 'good' course, it's a new gold standard in e-learning.