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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

OA

2017年9月8日

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

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:

626 - Applied Machine Learning in Python 的 650 個評論(共 1,454 個)

創建者 Ayon B

2018年10月19日

Good course. And challenging indeed, especially the quizzes.

創建者 Sathvik K

2018年8月28日

great for learning how to practically apply machine learning

創建者 sunil s

2018年7月5日

Great course for implementing machine learning using python.

創建者 Wai Y P S

2021年6月22日

Thanks you so much University of Michigan for Great course

創建者 Fernando G

2021年3月16日

Excellent course! Well paced and you end up learning a lot!

創建者 Hafiz A Q

2020年5月18日

A very nice course from the implementation point of view!!!

創建者 Mikhailov R

2019年1月27日

Sometimes the lecturer is boring but overall perfect course

創建者 Maciej W

2018年7月8日

Very informative, broad, hands-on course. Strong recommend.

創建者 nitin p

2018年2月28日

Very Interesting and fascinating Course of Machine Learning

創建者 Biju S

2017年10月12日

Very tough to finish. Big gap with material and assignments

創建者 Amoghavarsha B

2020年3月19日

Perfect course with lots of assignments and good material!

創建者 Yu S

2018年7月16日

Good applied material to study along theoretical material!

創建者 Marek S

2017年9月29日

Useful, practical use of sklearn to machine learning tasks

創建者 Matias B M

2017年8月14日

Challenging and rewarding. Wouldn't have it any other way.

創建者 Dipanjan S

2017年6月24日

Excellent clarity, recommended for getting started with ML

創建者 Mohd M K

2021年10月29日

Learned a lot!! Thanks coursera for this wonderful course

創建者 sung w c

2017年9月25日

Very well organized and useful for hands-on application.

創建者 Hiroki U

2020年11月29日

Last assignment was very good for understanding ML task.

創建者 Joga j

2022年1月24日

very good course and content.so many practice labs good

創建者 Mehrar I

2020年10月10日

this course is really helpful to learn machine learning

創建者 SAFVAN M S P

2020年8月17日

Amazing course, full of insights. Very well structured.

創建者 Thales A K N

2020年7月3日

Best Course in the Specialization!!! I learned so much!

創建者 Maryanne K

2020年5月4日

Great! Fun and useful course. Concepts explained well.

創建者 Ankush G

2020年1月14日

A good stepping stone towards a career in data science.

創建者 Fei W

2019年11月6日

The course is very well structured, highly recommended!