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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
8,059 個評分

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:

1276 - Applied Machine Learning in Python 的 1300 個評論(共 1,465 個)

創建者 Manuela D

2019年8月8日

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

創建者 Sang L

2018年7月28日

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

創建者 Lasal J

2020年12月29日

The content is great, I wish if there is more support in forums.

創建者 ABHISHEK B

2021年3月4日

Amazing Course for those who need to take the next step in ML.

創建者 Vatsal M

2020年5月26日

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

創建者 Jose I B L

2020年7月30日

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

創建者 Sai A D

2019年10月13日

pre-processing and unsupervised learning needs more emphasis

創建者 Deleted A

2018年7月13日

there are some gaps which is really difficult to understand!

創建者 Xingyu W

2019年10月13日

Need a better configuration for homework data file loading.

創建者 DENIS R

2020年5月22日

allowed me to hone my knowledge of machine learning models

創建者 Jason A

2018年6月26日

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

創建者 Bernardo A

2017年6月8日

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

創建者 Gururaj K

2021年11月11日

overall the course was good and has good content of data.

創建者 Manoj B

2020年6月2日

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

創建者 Antti H

2020年10月23日

Good course, but the labs have quite a few bugs in them.

創建者 Wang Y

2018年2月16日

Good, despite some confusions in the lecture and quiz.

創建者 Tangudu S S

2020年5月23日

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

創建者 Yash B

2020年5月7日

It was little bit difficult specially the assignments

創建者 Abhishek R

2018年5月27日

Needed a better retrospect on final/week 4 assignment

創建者 Alexander C

2018年3月11日

Good introductory course. A lot of material covered.

創建者 Dr. F T

2018年8月17日

Good but I was expecting much details in some area.

創建者 KOSHAL K

2020年3月1日

Its a very good course for an intermediate level.

創建者 Vinay P d L R

2017年9月26日

goes too fast and too shallow to deserve 5 stars

創建者 Adesh T

2022年2月8日

It was amazing journey to complete this course.

創建者 Prerna A

2021年4月27日

The course is planned in a very structural way.