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學生對 密歇根大学 提供的 Applied Machine Learning in Python 的評價和反饋

4.6
8,013 個評分
1,460 條評論

課程概述

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....

熱門審閱

FL

2017年10月13日

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

2020年11月26日

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.

篩選依據:

751 - Applied Machine Learning in Python 的 775 個評論(共 1,451 個)

創建者 Mariano S C

2019年10月25日

great course, excellent teaching staff

創建者 Shadi A

2019年5月6日

Best Instructor and simple explanation

創建者 Nilesh I

2018年10月5日

Practical, I liked the evaluation part

創建者 SANKURU M K

2020年5月17日

very nice course.usefull for beginers

創建者 Marion T

2019年5月31日

good introduction to machine learning

創建者 Michael T

2019年2月21日

Great content and reference materials

創建者 marco f

2022年3月9日

O​ne of the best I had with coursera

創建者 BITATA G

2020年8月26日

great course with very good content!

創建者 Tanishka M

2020年7月13日

Great course to master fundamentals!

創建者 A. Z M R

2019年6月8日

The auto grader should be error free

創建者 Mostafa H N Y

2019年6月1日

Very useful course. Thank you Kevyn.

創建者 Dingqiang Y

2019年3月22日

Good introduction with python tools.

創建者 Marcin C

2018年4月29日

Heavy, but extremely valuable course

創建者 Guneet B

2018年4月2日

High Quality resources and materials

創建者 Vladimír L

2018年1月18日

great course with a high value added

創建者 Dheeraj P

2017年8月24日

nice lecture series, Good Approach .

創建者 Yan

2017年7月5日

100% Free course as audit, recommend

創建者 Edward M

2021年10月4日

Great content ,, Greater instructor

創建者 Javad K

2021年3月24日

This course was very useful for me.

創建者 David W

2020年1月12日

A good introduction to Scikit learn

創建者 Navid A E

2018年10月16日

Absolutely the best professor ever!

創建者 Darren

2017年7月2日

Very Impressive and illustrative !!

創建者 Catherine L

2020年5月16日

Excellent course. I learned A Lot.

創建者 RICARDO D

2019年12月3日

Excellent material for intro to ML

創建者 Daniel H

2019年1月4日

Kevyn Collins-Thompson is a legend