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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
8,053 个评分

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

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.

OA

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

筛选依据:

1451 - Applied Machine Learning in Python 的 1465 个评论(共 1,465 个)

创建者 Vjaceslavs M

Apr 4, 2021

This course is outdated by few years and not been updated in general with lots of mistakes in assignments and on slides making it very not ejoyable to use.

创建者 David C

Nov 8, 2020

Not as good as prev. courses. Univ. of mic. should update or get ride of this module

创建者 Gallina S

Nov 19, 2021

G​ood curriculumn, nice assignments. Very poorly presented by the professor!!!

创建者 Paul C

Mar 27, 2021

Frankly the quiz questions are ridiculous and no explanation is given why answers are considered incorrect. The wording of the answers is not clear and any from 5 is 120 permutations. You get three attempts and then you have to wait 8 hours. Not great if you are studying part-time. I gave a star for the quality of the video which seemed good although I already know the theory from my university course. However, there was no written material - which again helps answer the questions. This is only a coursera courses, tests should be there to help learning not hinder it.

创建者 Dhawal M

Jan 13, 2022

There is no value addition after listening to the video lectures. You might as well just read the suggested Resources and attempt the Assignments on your own. I have never attended college and might assume that all college lectures are drab and monotonous.

创建者 Michael O S

Sep 16, 2021

There's a bug in the final homework that the TA and peers don't sufficiently explain how to solve so I can't get the course certificate just by knowing the content taught in the course. It's not fair.

创建者 Topiltzin H

Mar 22, 2021

Course was not as expected, I think XG Boost for instance is quite large and was covered in less than 20 minutes.

创建者 SAMADRITO B

Mar 19, 2021

The course is full of faulty assignment grader and the concepts given are not up to the mark

创建者 Aditya M

Jul 17, 2020

Can't the lecturer use proper slides with proper diagrams for a better explanation.

创建者 Deyner L P

May 29, 2022

Demasiados errores a la hora de enviar los laboratorios.

创建者 SHREYAS D

Aug 14, 2020

Things in the beginning are not explained properly

创建者 Joe R

Mar 31, 2021

Terrible lectures - assignments were good though

创建者 Varun D

Jul 19, 2022

A lot of the course has much to improve.

创建者 Konark Y

May 10, 2020

many issues while submitting assignments

创建者 Oleg G

May 16, 2020

enrolled by mistake want to u nenroll