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学生对 密歇根大学 提供的 Applied Machine Learning in Python 的评价和反馈

7,251 个评分
1,319 条评论


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....


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!!

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.


226 - Applied Machine Learning in Python 的 250 个评论(共 1,296 个)

创建者 Steven L

Apr 8, 2018

Very practical introduction to using Python for machine learning - less focused on theory and more focused on how to use the sklearn library and proper use cases for different classifiers and regressors.

创建者 Carlos D R

Dec 16, 2019

The course offers you a lots fot tools the face ML problems. There are few errors in the notebooks, but everyting is well documented in the forum. Good overview to represent data and train basic models.

创建者 Giorgio C

Aug 25, 2017

The course is well structured and covers all the most important topics. The programming assignment could be a bit more stimulating. Overall I'd recommend this course to everyone who's interested in ML.

创建者 Ewa L

Jun 17, 2017

Fantastic course! Great foundation on scikit-learn. Really focused on APPLYING machine learning with just enough information about the models themselves to understand what's going on behind the scenes.

创建者 AMIT S

Nov 27, 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.

创建者 Eduardo B

Jul 19, 2020

Pretty good for those who are not too familiar with all the statistics that happens "under the hood" in a machine learning algorithm. The name "applied" suits very well in that way. Congratulations!

创建者 Angelo S

Dec 20, 2018

An excellent resource to immerse yourself into machine learning methods. Professor Kevyn explains key concepts in the most intuitive way possible. It does require some previous experience in Python.

创建者 Petko S

Apr 3, 2018

Extremely useful course! You really get a lot of value from it and exactly what you would expect from such course! Very entertaining and a lot of additional educational materials! Thank You a lot!

创建者 shashank s

Aug 19, 2017

the content of videos , quiz and exercise all work extremely well together towards the stated goal of the course i.e. to give the learner a good over view of how to apply ML theories into action

创建者 Michael B

Jun 19, 2017

Not for the faint of heart and some experience with Python, in particular Pandas, is preferred. Great overview of the different methods used in machine learning. One of the better courses imo.

创建者 Brett S

Sep 18, 2020

Great content and good instruction. Need to fix the files in the assignments though. It's hard to keep track in the forums and frustrating go back and forth to find out why it's not working.


Apr 13, 2020

It feels good to learn something new and highly skilled demand in Engineering. Thanks to Coursera and instructor for providing such a wonderful opportunity of learning through your platform.

创建者 Jens L

Aug 20, 2018

Concise and clear presentation of the material with the majority of time focused around using TDD to learn and practice concepts through developing solutions to open ended coding challenges.

创建者 Amithabh S

Jun 23, 2017

Excellent course for someone who already has some knowledge of python but not quite familiar with machine learning. This course will teach you the application of machine learning in python.

创建者 Abdirahman A A

Jan 13, 2019

In depth course that covers a lot in a short amount of time. If you take some extra time to delve deeper into these topics, you can ensure a great overview of machine learning with python.

创建者 Diego A L B

Oct 22, 2020


Honestly, I never thought I could learn so much in an online course, excited for the rest of the specialization

创建者 Indrajit P

Mar 29, 2020

Very well structured and informative course ! All the lectures are concise and give enough context for self-exploration. The assignments provide are a good hands-on experience as well !!

创建者 jay s

Jul 15, 2017

Excellent lectures, good exercises to reinforce the material, and absolutely loved the explanations of the sophisticated mathematical models that made them more lucid and easy to digest.

创建者 Keary P

Mar 24, 2019

Great for high level concepts and practical applications of machine learning. After taking this course I feel more confident in my ability to work on real world machine learning tasks.

创建者 Andrew G

Aug 27, 2017

A lot of techniques packed into a relatively short course. Weeks 2 & 4 are noticably tougher than the other two, so allow plenty of extra time for assignment and quiz in those 2 weeks.

创建者 Tian L

Apr 20, 2020

it is a great course that covers the most important basics of the "traditional" machine learning and helps me build a solid foundation for more advanced machine learning topics later.

创建者 Alan H

May 8, 2019

Great course for the applications of machine learning. While I wouldn't recommend for someone with no ML experience, this was a great course for an R user trying to learn more python!

创建者 Rami A T

Jun 6, 2017

Very helpful and well-structured course, clear lecturing, and high-level assignments. I hope, however, if it can be offered another course specialized in unsupervised learning in ML.


May 5, 2020

Great Course. I love the way it is designed, delivered. I learned a lot. The most important part is that I enjoy every bit of the session and completed everything less than a week,

创建者 Muhammad A

Jun 8, 2018

I am just about to begins my Module 2 but I have realized that how much easy to understand and to the point course is. I would love complete it and be the proud scientist. Thanks.