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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

4.6
7,744 个评分
1,415 条评论

课程概述

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....

热门审阅

FL
Oct 13, 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!!

AS
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.

筛选依据:

1126 - Applied Machine Learning in Python 的 1150 个评论(共 1,399 个)

创建者 john w

Jan 29, 2018

Comprehensive and interesting course in Machine Learning. The use of Scikit Learn helps to give a concrete understanding of ML as well as how many specific algorithms can be utilized in real world problems.

创建者 Vishal S

Jun 23, 2018

It's a nice course. It'll familiarize you with different models, evaluation metrics and basics of machine learning and let you practice with some of the real world datasets during assignment.

创建者 Muzahidul A

Jul 7, 2020

assignments were so good. I think there was not enough information given for the quiz tests. And also the code given was not properly explained. But the materials were so good for practice

创建者 Raul M

Apr 28, 2018

A good introduction to algorithms available in python. I didn't give it a five stars because I 'm still confused on which algorithms to pick/use when I want to work on real data problem.

创建者 Julien Z

May 6, 2020

Very good mix of video and python notebook. Some improvement can be done with the AutoGrader like get back the error python stack trace.

Globally, very good course - strongly recommanded

创建者 Kai K

Jun 4, 2018

The final assignment passing was a little too east,

there not being need to use fully what I learnt.

Still,the overall course was very good, and I am willing to keep on take other courses.

创建者 Vinicius d A O

Mar 16, 2020

This course was very good, with a lot of information and important tips for me. The instructor is good but he is long winded, so this course was very long with videos during 20 minutes.

创建者 Saman A

Aug 15, 2019

- more technical materials, comparisons and better classified details should've been provided, especially to be more proportional to the assignments.

-again, subtitles were full of typos

创建者 philippe p

Jun 7, 2017

The course is well balanced but the progression becomes quite agressive at Week3 and culminate at Week4 with a real life case assignment without much guidance. Great experience dough.

创建者 Vaishnavi M

Jun 29, 2020

Amazingly explained. An intermediate Machine Learner would definitely get clarity of concepts already learned and also new concepts explained so skillfully with graphs and diagrams.

创建者 Alex E

Aug 27, 2018

Good overview of methods in ML. Would have been nice if the lectures contained a little more mathematical rigor and explanation of why and how the various algorithms are effective.

创建者 Virgil C L

Feb 13, 2018

Good course and prof.

The exam and exercise in very interesting according to what I learn in following all videos, with this i improved my level in python progamming, I recommended.

创建者 Eugene S

Jul 3, 2017

Automatic assignment grader has room for improvement. Some python code that works perfectly well when run locally or on the course web page would crash when run by autograder.

创建者 Jiunjiun M

Mar 7, 2018

The class material is well prepared and make machine learning very easy to learn. The first three homework assignment is a bit hand-holding but the last one is really good.

创建者 Amine D

Oct 22, 2019

Good Course, i would have liked a little bit more theory about the algorithms, but this is an applied course of ML. Projects are good and the readings are interessting!

创建者 Gautam P

Nov 20, 2017

Videos are good and had challenging assignments. I enjoyed learning new concepts. I wish we had one more week to practice more on advanced Machine learning concepts.

创建者 Giovanni S

Jun 16, 2020

Very interesting, a lot of focus of statistica theory and little less (as compared to previous courses of specialization) on practical examples and implementation.

创建者 Jiangzhou F

Jun 23, 2020

Good overall but some concepts and python functions need more explanations. Maybe 5 or 6 weeks are more appropriate for this course. It is too dense under 4 week.

创建者 Holden L

Aug 31, 2019

better than the first two courses of this specialization for the content is coherent and the assignment is relevant to the knowledge taught in the course video.

创建者 Leon V

Jul 2, 2017

Request: Can we have the instructions with a "translation" to "regular" English - for those of us who still have to get used to machine learning jargon? Thanks.

创建者 Christian P

Aug 5, 2019

Code and examples were very useful. Teaching a bit lengthy and detailed at times. Overall a very good course for getting hands-on machine learning in python.

创建者 Weiqi Y

Oct 24, 2017

It's alright as a course focusing on applied techniques. If you are expecting more theories and understanding of the algorithms, this one may not for you

创建者 Sidharth R

May 9, 2021

the authors can include more coding questions so as to not only help a student to learn Machine learning but also become fluent in implementing it.

创建者 Helen L

Jun 15, 2020

Submission isnt easy often gave errors that are not due to students' faults. Time-consuming unnecessarily. The content and assignments are great.

创建者 Utkarsh S

Jun 22, 2020

Very informative course, the only issue I had was with the file locations in the assignments. Takes up a lot of time switching back and forth.