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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
6,166 个评分
1,108 条评论

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

FL

Oct 14, 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!!

OA

Sep 09, 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:

951 - Applied Machine Learning in Python 的 975 个评论(共 1,092 个)

创建者 Arun P P

Jul 29, 2020

It was n insightful course but was quite advanced for a beginner.

创建者 Manuela D

Aug 08, 2019

Well organised, lots of details, a good overview of ML algorithms

创建者 Sang L

Jul 28, 2018

Speed kinda fast but maganeable. Need more detiailed notes/slides

创建者 vatsal m

May 26, 2020

Some of the assignments have bugs in them please rectify them.

创建者 Jose I B L

Jul 31, 2020

Good coure, need more feedback in the quizzes and asigments.

创建者 Anuhya D

Oct 14, 2019

pre-processing and unsupervised learning needs more emphasis

创建者 Deleted A

Jul 13, 2018

there are some gaps which is really difficult to understand!

创建者 Xingyu W

Oct 14, 2019

Need a better configuration for homework data file loading.

创建者 DENIS R

May 23, 2020

allowed me to hone my knowledge of machine learning models

创建者 Jason A

Jun 26, 2018

This course was tougher than expected, but I learned a lot

创建者 Bernardo A

Jun 08, 2017

Great content and good assignments! Learned a lot from it.

创建者 Venkata S M B

Jun 02, 2020

Decent course. I'd call this, 'Intro to Machine Learning'

创建者 Wang Y

Feb 16, 2018

Good, despite some confusions in the lecture and quiz.

创建者 Tangudu S S

May 23, 2020

Got a very clear picture of ML usage in Data Science.

创建者 yash b

May 07, 2020

It was little bit difficult specially the assignments

创建者 Abhishek R

May 27, 2018

Needed a better retrospect on final/week 4 assignment

创建者 Alexander C

Mar 11, 2018

Good introductory course. A lot of material covered.

创建者 Tarrade F

Aug 17, 2018

Good but I was expecting much details in some area.

创建者 KOSHAL K

Mar 01, 2020

Its a very good course for an intermediate level.

创建者 Vinay P d L R

Sep 26, 2017

goes too fast and too shallow to deserve 5 stars

创建者 Anendra G

Apr 30, 2018

Awesome theory about machine learning concepts.

创建者 Harsh A

Feb 04, 2018

Good course.

Thanks to entire team

Harsh Arora.

创建者 XJTLU

Jun 19, 2019

Some concepts should be introduced in detail.

创建者 Amita D

May 18, 2018

Need more information about more algorithms

创建者 Ruben W

Sep 08, 2019

Best course so far in this specialisation