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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

8,013 个评分
1,460 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....



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.


Sep 8, 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


1401 - Applied Machine Learning in Python 的 1425 个评论(共 1,451 个)

创建者 Om R

Apr 26, 2020

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

创建者 Darshan S

Dec 31, 2019

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

创建者 Mauricio A E G M

Nov 17, 2019

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

创建者 Nikola G

Jan 14, 2019

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

创建者 Chirag S

May 24, 2020

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

创建者 Rohit S

May 21, 2020

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

创建者 Gilad A

Jun 27, 2017

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

创建者 Sai P

Jun 3, 2020

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

创建者 Philip L

Oct 31, 2017

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

创建者 Dileep K

Oct 3, 2021

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

创建者 Sundeep S S

Apr 4, 2021

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

创建者 Iuri A N d A

Aug 4, 2021

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

创建者 Pakin P

Jan 10, 2020

How can i pass without reading discuss about problem with notebook

创建者 Hao W

Aug 27, 2017

The homework is too easy to improve our understanding of ML

创建者 M S V V

Jun 29, 2020

Too much of information compressed within a short span.

创建者 José D A M

Jun 21, 2020

Too fast, yet too difficult. Needs deeper explanation.

创建者 Navoneel C

Nov 21, 2017

Nice and Informative but not practically effective

创建者 Priyanka v

May 8, 2020

if it is more detailedthen it will be more useful

创建者 Sameed K

Mar 15, 2018

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

创建者 Andy S

Jun 4, 2019

It could have been better with more examples.

创建者 Syed S

Apr 12, 2020

The explanation could have been much better.

创建者 Sagar J

Mar 21, 2021

Good start but i was very boring later on.

创建者 Jeremy D

Jul 10, 2017

The topics were good, but too many were d

创建者 Ryan S

Dec 12, 2017

Homeworks are inconvenient to submit


May 16, 2020

The narration was a bit boring.