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

7,745 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

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!!

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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1351 - 1375 de 1,399 revisiones para Applied Machine Learning in Python

por Ankur P

30 de mar. de 2019

Unsupervised learning was missing. The codes written in the lectures were not explained clearly. Some topics looked unimportant.

por James F

13 de feb. de 2018

Good overview of methods. A bit too intense at times though, may have been better to really focus on a couple of key concepts.

por Om R

26 de abr. de 2020

The course is great, but need certain improvement for assignments and quizzes. The facts should be checked multiple times.

por Darshan S

31 de dic. de 2019

Not enough real life examples throughout the video, makes it very hard to concentrate during the whole lecture.

por Mauricio A E G M

17 de nov. de 2019

This course is not useful to learn from scratch, but has some good things, for example the final assignment.

por Nikola G

14 de ene. de 2019

Really didn't like the quiz parts of the course. If it was up to me I would do thorough revision of these.

por Chirag S

24 de may. de 2020

The content was less informative and audio quality was poor. However, assignments are fun completing.

por Rohit S

21 de may. de 2020

The online grader needs to be updated as there is constant error showing up though our code is right

por Gilad A

27 de jun. de 2017

The last assignment was super. apart for it, the assignments and the course were too easy

por Sai P

3 de jun. de 2020

There were a few corrections made during the videos which ended being quite confusing.

por Philip L

31 de oct. de 2017

The assignments are extremely difficult, professor is a bit dry during lectures.

por Dileep K

3 de oct. de 2021

Although content is really helpful, assignment part has many technical issues!

por Sundeep S S

4 de abr. de 2021

Only classification based ML is covered. Regression based ML is non-existant.

por Iuri A N d A

4 de ago. de 2021

It has potential, but the assignment evaluation had a lot to be fixed.

por Pakin P

10 de ene. de 2020

How can i pass without reading discuss about problem with notebook

por Hao W

27 de ago. de 2017

The homework is too easy to improve our understanding of ML

por M S V V

29 de jun. de 2020

Too much of information compressed within a short span.

por José D A M

21 de jun. de 2020

Too fast, yet too difficult. Needs deeper explanation.

por Navoneel C

21 de nov. de 2017

Nice and Informative but not practically effective

por Priyanka v

8 de may. de 2020

if it is more detailedthen it will be more useful

por Sameed K

15 de mar. de 2018

have to figure out a lot of things on you own.

por Andy S

4 de jun. de 2019

It could have been better with more examples.

por Shan J

12 de abr. de 2020

The explanation could have been much better.

por Sagar J

21 de mar. de 2021

Good start but i was very boring later on.

por Jeremy D

10 de jul. de 2017

The topics were good, but too many were d