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

7,981 calificaciones
1,452 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


8 de sep. de 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses


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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1401 - 1425 de 1,442 revisiones para Applied Machine Learning in Python

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 Syed S

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

por Ryan S

12 de dic. de 2017

Homeworks are inconvenient to submit


16 de may. de 2020

The narration was a bit boring.

por shreyas

29 de jun. de 2020

Teacher wasn't very good

por Abir H R

30 de jun. de 2020

very long videos

por Wojciech G

28 de oct. de 2017

To fast paced.


10 de abr. de 2022



29 de oct. de 2021


por Aarya P

30 de sep. de 2020

Really disappointed with the course may ask why??

The first thing is the instructor , super boring. The instructor (with all due respect) was very dry and the lectures were super uninteresting. When he keeps on talking code, but doesn't really explain stuff. The material and lectures were dry and colorless.

Me without having good statistics background had huge difficulties understanding the concepts. Please i recommend everyone to have good knowledge in statistics before starting the course. ABSOLUTELY NOT THE BEGINNER LEVEL AND NEITHER INTERMIDIATE LEVEL .the course is quiteeeee difficult.

You also need to have a lot of self study , which i am not a big fan of. I hope they make the course more fun rather than a man constantly talking on the screen .

por Daniel J

30 de abr. de 2021

I found this course quite challenging to complete. The assignments are difficult (which is good, they are practical and I enjoyed them) and only a fraction of things is explained in the videos. I really found much better learning materials around the web (and for free!). For applied machine learning course, I would expect more practical videos. Also the process of submitting assignments is really frustrating, I spent half the time correcting errors that were not related to the assignment objective. If this course was not part of specialization, I would not complete it.

por Douglas H

10 de abr. de 2021

Lectures are good but they expect you to extract too many fine details from them in order to pass the quizzes and assignments. You'd have to watch these oral lessons ten times in order to pass the tests, which are needlessly nitpicky.