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Отзывы учащихся о курсе Applied Machine Learning in Python от партнера Мичиганский университет

Оценки: 6,563
Рецензии: 1,176

О курсе

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....

Лучшие рецензии


Sep 09, 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


Oct 14, 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!!

Фильтр по:

1051–1075 из 1,157 отзывов о курсе Applied Machine Learning in Python

автор: xingkong

Aug 09, 2017

quiz is harder than assignment.

автор: shreyash t

Jul 28, 2020

overalll good way to start ml

автор: Vaibhav S

May 27, 2020

way better than last teacher.

автор: Nicolas B

Jul 05, 2017

Muy buen curso, muy completo.

автор: 李祥泰

Aug 15, 2017

Nice courses with nice quiz!

автор: 刘倬瑞

Jul 29, 2017

Useful, though a little easy

автор: Landon M L

Jul 09, 2017

the discussion forum is good

автор: Rizvaan M

May 21, 2020

This was a great course.

автор: yassin b m

Jul 17, 2020

Good Course , Thanks!!

автор: Burak

Sep 30, 2018

good for scikitlearn.

автор: Bama

Jul 11, 2020

This course is good.

автор: Abhav T

Jun 03, 2020

Nice course to study

автор: Shashi K

May 18, 2020

very good learning

автор: hamzaoui m

Jul 25, 2019


автор: Aditya V

Jul 03, 2018


автор: ISHAN H S

Jul 23, 2017

Awesome !!!

автор: KILLANI T

Jun 10, 2020

hard a bit

автор: Deepak

Jan 13, 2020

Very Good

автор: Md J A

Aug 19, 2017

very good

автор: MOHD A

Sep 10, 2020


автор: Sajal P

Aug 12, 2020


автор: Latha B N

Jul 09, 2020


автор: Yzeed A

Oct 30, 2019


автор: Ketan S R

Jul 04, 2019


автор: Navish A

Jul 20, 2020

I just completed the third course (Applied Machine Learning course) over the last 7 days.


The course syllabus is quite well designed for an applied intro ML course

Assignments are nice & force you to think; you cannot simply watch the lectures & complete them straightaway; which is good in my opinion.

Needs to Improve:

The lectures are atrociously boring. The professor seems to be reading out from a teleprompter in a flat pitch.

There are parts where the intuition behind the concepts are well explained and others where you are left staring at stars and better off learning from other sources over the net.

The course seems to have been all but abandoned. Common mistakes in the assignment setup & lecture recordings have not been corrected since the course was first offered 2.5 years ago. The discussion forums keep getting spammed on similarly asked questions which can be easily solved by correcting the assignment errors and providing a few clearer comments/instructions. Week 3 lectures definitely need to be re-recorded as there is a correction prompt on every video. There is one 'Mentor' who helps out as a volunteer. No one else to moderate the forums.

The course pace is quite uneven and patchy. Week 2 is extremely heavy while week 1 super light. Week 3 is good but week 4 feels half done/rushed. Seems like there is an arbitrary administrative requirement to do a four week course from UMich.

All in all, I did not come away impressed & elated from the course. I did expect much better from my Alma Mater.