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学生对 University of Michigan 提供的 Applied Machine Learning in Python 的评价和反馈

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8,605 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.

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!!

筛选依据：

676 - Applied Machine Learning in Python 的 700 个评论（共 1,574 个）

创建者 nitin p

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Feb 28, 2018

Very Interesting and fascinating Course of Machine Learning

创建者 Biju S

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Oct 11, 2017

Very tough to finish. Big gap with material and assignments

创建者 Amoghavarsha B

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Mar 19, 2020

Perfect course with lots of assignments and good material!

创建者 Leonadis

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Jul 16, 2018

Good applied material to study along theoretical material!

创建者 Marek S

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Sep 29, 2017

Useful, practical use of sklearn to machine learning tasks

创建者 Matias B

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Aug 14, 2017

Challenging and rewarding. Wouldn't have it any other way.

创建者 Dipanjan S

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Jun 23, 2017

Excellent clarity, recommended for getting started with ML

创建者 MOHSIN K 2

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Oct 28, 2021

Learned a lot!! Thanks coursera for this wonderful course

创建者 sung w c

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Sep 24, 2017

Very well organized and useful for hands-on application.

创建者 Hiroki U

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Nov 29, 2020

Last assignment was very good for understanding ML task.

创建者 Yujin Y

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Oct 13, 2022

I could learn a lot from this course. a great course.

创建者 Joga j

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Jan 24, 2022

very good course and content.so many practice labs good

创建者 Mehrar I

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Oct 10, 2020

this course is really helpful to learn machine learning

创建者 SAFVAN M S P

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Aug 17, 2020

Amazing course, full of insights. Very well structured.

创建者 Thales A K N

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Jul 3, 2020

Best Course in the Specialization!!! I learned so much!

创建者 Maryanne K

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May 4, 2020

Great! Fun and useful course. Concepts explained well.

创建者 Ankush G

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Jan 14, 2020

A good stepping stone towards a career in data science.

创建者 Fei W

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Nov 6, 2019

The course is very well structured, highly recommended!

创建者 Nikhil N

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Jul 6, 2021

Wonderful Course but slightly difficult for beginners

创建者 Kunal K

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Apr 22, 2020

it good to basics and devloped the skill in that field

创建者 Ajay S

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Jan 28, 2019

great course thanks for financial aid for the course .

创建者 Kristin A

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Jan 12, 2019

Great intro to the tools of machine learning in Python

创建者 Walter M

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Jul 29, 2018

Good class. The asignements made me a better engineer.

创建者 Alonso S A

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Nov 10, 2017

Very usefull, easy to understand and full of examples.

创建者 Кочеткова А М

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Dec 8, 2020

Interesting lectures, everything is clear, thank you!