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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
8,060 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:

701 - Applied Machine Learning in Python 的 725 个评论(共 1,465 个)

创建者 Arturo R

Jul 15, 2021

Very well balanced between dificuty and learning

创建者 Manan S

May 25, 2020

Awesome Teaching and Assignemts are very usefull

创建者 DEBAYAN M

Jun 4, 2019

A must -learn for every aspiring data scientist.

创建者 Mischa L

Jan 6, 2018

Great course with excellent homework assignments

创建者 Shivani R

Jul 19, 2020

Very good course. Detailed videos & explanation

创建者 SRIHARI

Jul 18, 2017

This is good course gives in depth information.

创建者 M.Fauzan A

Jan 1, 2021

thanks for knowledge and live to inspire,peace

创建者 Ana K A d M

Feb 7, 2020

Excellent balance between theory and practice!

创建者 Krishna P S

Mar 1, 2018

Excellent course. Nicely designed & delivered.

创建者 Eray Ö

Sep 26, 2017

great course with a lot of hands-on experience

创建者 Mohammad Q M A

Jul 13, 2020

It is A great course ! I recommend to take it

创建者 Punam P

Apr 15, 2020

Very Nice Course..I really Enjoyed it..Thanks

创建者 Yongqing H

Aug 5, 2019

It's so hard. But every endless trying worth.

创建者 Dario M

Jul 12, 2019

So far the best course in this specialization

创建者 Rohit M S

Mar 22, 2019

The Course is amazing. you get to learn a lot

创建者 Xiaoyue Z

Jul 30, 2018

A very helpful and confidence-building class!

创建者 Ruyang L

Apr 20, 2018

Very interesting course, enjoyed it very much

创建者 zios s

Nov 23, 2017

great course very useful in data science job.

创建者 Om P

May 17, 2020

perfect for beginners! thank you, professor!

创建者 Pilar V

Sep 14, 2019

Super interesting course and specialization!

创建者 Joan P

Nov 5, 2017

Very interesting last programming assignment

创建者 David M

Jul 7, 2017

Great introduction to Scikit-learn tool set.

创建者 Danish R

Jul 2, 2017

P.S.: This is not an easy course to complete

创建者 Amey k

Jan 9, 2022

best course for machine learning enthusiast

创建者 sudipta d

Oct 29, 2021

this course helps me to building my skills.