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미시건 대학교의 Applied Machine Learning in Python 학습자 리뷰 및 피드백

7,970개의 평가
1,450개의 리뷰

강좌 소개

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....

최상위 리뷰


2017년 9월 8일

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


2020년 11월 26일

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.

필터링 기준:

Applied Machine Learning in Python의 1,442개 리뷰 중 1401~1425

교육 기관: Philip L

2017년 10월 31일

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

교육 기관: Dileep K

2021년 10월 3일

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

교육 기관: Sundeep S S

2021년 4월 4일

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

교육 기관: Iuri A N d A

2021년 8월 4일

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

교육 기관: Pakin P

2020년 1월 10일

How can i pass without reading discuss about problem with notebook

교육 기관: Hao W

2017년 8월 27일

The homework is too easy to improve our understanding of ML

교육 기관: M S V V

2020년 6월 29일

Too much of information compressed within a short span.

교육 기관: José D A M

2020년 6월 21일

Too fast, yet too difficult. Needs deeper explanation.

교육 기관: Navoneel C

2017년 11월 21일

Nice and Informative but not practically effective

교육 기관: Priyanka v

2020년 5월 8일

if it is more detailedthen it will be more useful

교육 기관: Sameed K

2018년 3월 15일

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

교육 기관: Andy S

2019년 6월 4일

It could have been better with more examples.

교육 기관: Syed S

2020년 4월 12일

The explanation could have been much better.

교육 기관: Sagar J

2021년 3월 21일

Good start but i was very boring later on.

교육 기관: Jeremy D

2017년 7월 10일

The topics were good, but too many were d

교육 기관: Ryan S

2017년 12월 12일

Homeworks are inconvenient to submit

교육 기관: PIYUSH A

2020년 5월 16일

The narration was a bit boring.

교육 기관: shreyas

2020년 6월 29일

Teacher wasn't very good

교육 기관: Abir H R

2020년 6월 30일

very long videos

교육 기관: Wojciech G

2017년 10월 28일

To fast paced.

교육 기관: PRAGATHI S P 2

2022년 4월 10일


교육 기관: TANMAY B

2021년 10월 29일


교육 기관: Aarya P

2020년 9월 30일

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 .

교육 기관: Daniel J

2021년 4월 30일

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.

교육 기관: Douglas H

2021년 4월 10일

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.