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Opiniones y comentarios de aprendices correspondientes a Applied Machine Learning in Python por parte de Universidad de Míchigan

4.6
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7,749 calificaciones
1,415 reseña

Acerca del Curso

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....

Principales reseñas

FL
13 de oct. de 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!!

AS
26 de nov. de 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.

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1151 - 1175 de 1,400 revisiones para Applied Machine Learning in Python

por Utkarsh S

22 de jun. de 2020

Very informative course, the only issue I had was with the file locations in the assignments. Takes up a lot of time switching back and forth.

por Mariano T

18 de may. de 2020

There are some problems with the assignments but the course is very good. You must improve the material for the assiggnment. I love the forum

por Srinivas R

22 de sep. de 2017

Good overview of machine learning topics with practical exercises in the use of multiple techniques primarily through use of scikit-learn.

por David W

3 de jul. de 2017

Hands on and practical. Dr. CT and his staff have done a great job introducing Machine Learning. Where were you 20 years ago? Thank you!

por Rakshit T

10 de jul. de 2018

A good course for beginners in Machine Learning. You get to the learn the basics of many techniques and their implementation in python.

por yannick t

12 de abr. de 2018

Excellent lectures. However, I would have needed more guidance for the last assignment. I learned a lot, but through pain and struggle.

por M V B

9 de oct. de 2020

It was a great experience learning through Coursera ,who provides best faculty for making students understand easily.

thank you Cousera

por GC

31 de mar. de 2021

This course is useful, but the code is not updated, and the assignment and Module codes returned a lot of code deprecation warnings.

por Prathmesh D

15 de jul. de 2020

It was a great learning with you all got little problems but solved as per instructions and they helped me through that,thanking you

por PRATIKKUMAR A P

23 de ago. de 2020

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ience of machine learning using python. Very well explained algorithms and application through modules and assignments.

por p180054 M I

27 de ago. de 2021

Best Course if you are searching for the applied side of Machine learning and Assignment are very helpfull to make mucssle memory

por MARCO S M H

7 de feb. de 2021

excellent course, except for the last week. I think that the last part about decision trees, NN and randomforest could be better

por Dr. P R K

23 de ene. de 2018

Unlike the name suggests, this course only covers the Supervised learning side of the ML. However, the supervised side is good.

por Michael S

29 de jun. de 2019

Everybody has different skill levels, but this was really hard and really, really, really fast.

Did I say it was really fast?

por New_diver N

22 de may. de 2019

Course content is very nice and covered aptly. I feel that some where more depth was necessary to understand the algorithms.

por bob n

31 de ago. de 2020

Tough, but fair weekly assessments. Lecturer is a bit on the dry, boring side. Be careful not to let you attention drift.

por BHAGYASHREE B

9 de may. de 2020

Other than the subtle mistakes, the overall course was very informative. I wish there were more practise exercises though

por Mohamed S

26 de mar. de 2020

A comprehensive course by a wold class university,some teaching could have been better by using more interactive methods.

por Amaira Z

12 de ene. de 2021

Well explained course with good material in python, may be an additionnal week is needed for the unsupervised learning

por Ekun K

16 de jul. de 2020

This is a great course. I recommend using the Introduction to Machine Learning book to complement the lecture videos.

por Wynona R N

23 de jun. de 2020

Good introduction course on machine learning algorithms. The books and the readings are recommended to look through!

por Amanda V

2 de jun. de 2018

You will learn a lot. But the course is a little bit fast for regular students. Assignments deal with real problems.

por Rohith S

16 de nov. de 2017

A few more code examples would have helped better understand various packages provided by Python and how to use them

por lcy9086

2 de feb. de 2019

Great course on doing machine learning use sklearn and put little but enough explanation of the theories behind it!

por Alexandr S

24 de feb. de 2019

It would be nice to have more practical assignments like the last one! Anyway it was very interesting! Thank you!