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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：

801 - Applied Machine Learning in Python 的 825 个评论（共 1,574 个）

创建者 Marion T

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May 30, 2019

good introduction to machine learning

创建者 Michael T

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Feb 21, 2019

Great content and reference materials

创建者 marco f

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Mar 9, 2022

One of the best I had with coursera

创建者 BITATA G

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Aug 26, 2020

great course with very good content!

创建者 Tanishka M

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Jul 12, 2020

Great course to master fundamentals!

创建者 A. Z M R

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Jun 8, 2019

The auto grader should be error free

创建者 Mostafa H

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Jun 1, 2019

Very useful course. Thank you Kevyn.

创建者 Dingqiang Y

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Mar 22, 2019

Good introduction with python tools.

创建者 Marcin C

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Apr 29, 2018

Heavy, but extremely valuable course

创建者 Guneet B

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Apr 2, 2018

High Quality resources and materials

创建者 Vladimír L

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Jan 18, 2018

great course with a high value added

创建者 Dheeraj P

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Aug 24, 2017

nice lecture series, Good Approach .

创建者 Yan

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Jul 5, 2017

100% Free course as audit, recommend

创建者 Edward M

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Oct 4, 2021

Great content ,, Greater instructor

创建者 Javad K

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Mar 24, 2021

This course was very useful for me.

创建者 David W

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Jan 12, 2020

A good introduction to Scikit learn

创建者 Navid A E

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Oct 16, 2018

Absolutely the best professor ever!

创建者 Darren

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Jul 2, 2017

Very Impressive and illustrative !!

创建者 Catherine L

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May 16, 2020

Excellent course. I learned A Lot.

创建者 RICARDO D

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Dec 3, 2019

Excellent material for intro to ML

创建者 Daniel H

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Jan 4, 2019

Kevyn Collins-Thompson is a legend

创建者 Syam P N

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Dec 16, 2018

Excellent course. Was very helpful

创建者 Sudhir T

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Aug 1, 2018

nice course and easy to understand

创建者 Armand L

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Apr 24, 2018

Very Good Course ! learned a lot !

创建者 Oleg D

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Mar 24, 2018

ONE OF THE BEST THAT ONE CAN FIND!