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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

8,053 个评分


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....



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.


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


801 - Applied Machine Learning in Python 的 825 个评论(共 1,465 个)

创建者 Dinith M

Nov 19, 2020

Learned a lot, excellent content

创建者 Gaurav R

Oct 21, 2020

best course for machine learning

创建者 Saurabh D

May 24, 2020

Very hands on. I learnt so much.

创建者 Priscila L

Nov 10, 2018

nice teacher, interesting course

创建者 Sayan G

Jun 15, 2018

Exhaustive and in depth coverage

创建者 Guido L

Feb 8, 2018

Very good, comprehensive course!

创建者 Paul M S

Jul 31, 2017

Very informative and educational

创建者 José E L

Jun 6, 2017

Excellent material and tutoring!

创建者 Jun S

Dec 30, 2020

Excellence course for beginners

创建者 Adish P

May 28, 2020

Excellent Course. Very helpful.

创建者 Vikas K

Dec 9, 2019

best course in detailed version


Jul 17, 2019

excellent course for beginners!

创建者 Fadhel A

May 28, 2019

whole new informations for me.

创建者 Li T

Oct 28, 2017

Informative course. Five stars!

创建者 Ivan P

Aug 14, 2017

Very good course for beginners)

创建者 Madalina-Mihaela B

Jul 18, 2017

Awesome course. Very practical!

创建者 Jim S

Jul 1, 2017

Excellent content and delivery.

创建者 Keziah S T K S

Dec 6, 2021

Good explanations, good module

创建者 dean w

Nov 15, 2020

Very Challenging and Rewarding

创建者 Koshanov A

Oct 15, 2020

очень удобно, кратко и понятно

创建者 Paul S

Apr 16, 2020

Very interesting and enjoyable

创建者 Elizabeth N

Apr 2, 2020

Very good applied introduction

创建者 Muhammad Z H

Sep 1, 2019

Learnt a lot Professor. Thanks


Aug 6, 2019

Nice course, Practice oriented

创建者 Jan P

Jul 11, 2018

great course - I learnt a lot!