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

4.6
stars
7,295 ratings
1,331 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.

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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176 - 200 of 1,308 Reviews for Applied Machine Learning in Python

By Christos G

Sep 1, 2017

Following the first 2 sessions of this specialisation, this one seems easy and gives the student a lot of confidence. Make sure you follow the sequence suggested in this specialization, even if you do not plan to continue with Text Mining and Social Networks.

By John B

Mar 18, 2018

Challenging but worthwhile mix of essential theory (explained well) and hand-on practice with good, sensible exercises to help one get a confident grasp of scikit learn packages which one can use in the real world. Many thanks to the organisers and Coursera.

By Naman M

Feb 26, 2019

The Instructor is marvelous. The Assignments are amazing, The TA is really responsive. The content only for one month course was outstanding, my feedback would be to increase the amount of exercises(coding) and assignments, and make the course for 2 months.

By Jonathan B

Jul 14, 2020

Excellent introduction into machine learning with Python. I came into this class with little knowledge of machine learning and was taking this to aid in my data science career. As a result of this course, I've decided to focus more on machine learning.

By Melissacrawford

May 6, 2020

This course does a really good job taking you through the basics of ML through use of Scikit Learn models. It goes over a broad swath of models in a black-box fashion so you can start getting a feel for how each model is tuned and what parameters to use.

By Farzad E

Mar 14, 2019

Assignments and quizzes help you a lot in consolidating the concepts. However, some questions in quizzes are tricky but not in a way that really adds to your understanding of the topic. Overall a pretty good course. (4.5/5 is the rating I would give)

By Roger C

Feb 12, 2021

This course is well-structured and I learned a lot from it. Students who use retrieval practice, which is a form of self-testing, retain the information longer and learn better. I liked the quizzes and the assignments, and I wished there were more.

By Amitava C

Apr 18, 2020

The course content is excellent and the instructor makes stuffs easier. Few assignments are very tough but if you go through the course properly can able to solve it. One request to the instructors to a bit slow the pace for better understanding. :)

By 谢仑辰

Mar 7, 2018

Though it just give us a limited amount of information about Machine Learning, it really drive me into the novel world of this field.The course told me a lot of basic concepts about ML, thus I can go through many thesis related to the realm, thanks.

By 전하림

Jan 1, 2021

Very practical lectures for implementing machine learning. Provides more hands on experience and you can get familiar with python machine learning libraries with this course. Highly recommend if you want to really practice machine learning coding.

By H.-M. F C

Jan 26, 2019

The course ire great and illustrates many useful topics. The only thing it needs to improve is about the assignment 4 which requires more information to solve the problem, in particular, people who deal with the complete machine learning problem.

By Olin S

Jan 6, 2019

The programming assignments where though because the automatic grader was very picky. Please change it so it gives the user more input about what part of their code is wrong. Also Have a repository where the user can retrieve previous submissions.

By reddi m

Apr 18, 2020

Excellent course !!!!! very useful for people who have just completed python and wanted to apply the language. Much more clear when we do the course after studying the libraries of python , very clear explanation throughout the entire course .

By Oj S

Jun 1, 2020

It was a great learning experience. The way the course structure is curated is truly adapting to the current trends in field of ML and AI. Thank you for giving me an opportunity to learn from best teachers on a great online learning platform.

By Martin G

Jun 22, 2020

Fantastic course theory and material. Additional vague pointers would have been useful for Assignment 4 to help understand required data manipulation not included in the notebooks.

Many thanks to the team and Professor Kevyn Collins-Thompson

By LENDRICK R

Apr 7, 2019

A ton of learning, a challenging & rewarding course, the final assignment incorporated concepts & techniques from the first and second courses and gave me a clearer understanding of choosing and implementing machine learning algorithms. :-)

By Dinesh M

Feb 28, 2021

The course had the right amout of labs and lectures to experiement the different algorithms and their theory. The auto grader took a while to understand but the the discussion forum threads were immensely helpful, particularly of Sophie's.

By Brian R v K

Oct 29, 2017

This was a great course, with broad coverage of the topic and practical application in Python with scikit-learn. Challenging quizzes were part of the learning context. Overall a great experience, and the best course in the specialization.

By Yusuf E

Jul 31, 2018

Excellent overview of many ML algorithms. Challenging quizzes and assignments. The only downside is that some functions like fit_transform, decision_function, predict_proba could have been explained a little better. Great coverage though.

By David A d A S

Jul 31, 2017

Awesome.

I learned a lot of fundamentals machine learning. The lectures are very clear and the assignaments focus on practical examples.

I recomend this course for everyone who want to have a global view of machine learning.

I enjoyed a lot.

By Michael D

Jul 19, 2017

I thought this was a fascinating course that tried to do the near impossible and succinctly summarise the key techniques of machine learning. And it did that very well. Very challenging tasks, but also overall inspiring for the next step.

By Vishesh G

Sep 8, 2018

This was an amazing course that I absolutely loved working on. It gave a deep insight into machine learning. I gained a lot of knowledge from this course. A must for the students who are just stepping in the field of Machine Learning.

By Arturo B E G

May 31, 2020

It's a nice course, that accomplishes what it promised: overviewing ML algorithms from an applied perspective; however, I think that some other model selection methods (especially when comparing regressions) should have been included

By Ganesh K

Apr 14, 2018

Tough and exhausting, but thoroughly worth it. I learnt a lot - and I already knew machine learning before taking this course. Be prepared to spend a lot of time preparing for the quizzes. The assignments are easier than the quizzes.

By Manikant R

May 9, 2020

The course is well taught, by covering a lot of topics in short time, Yes you have to research a lot to get a full understanding, as the ML itself is not easy, you have to do hard work. I liked the references provided in the course.