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学生对 University of Michigan 提供的 Applied Machine Learning in Python 的评价和反馈

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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

MB

Jun 18, 2017

Not for the faint of heart and some experience with Python, in particular Pandas, is preferred. Great overview of the different methods used in machine learning. One of the better courses imo.

PS

Apr 2, 2018

Extremely useful course! You really get a lot of value from it and exactly what you would expect from such course! Very entertaining and a lot of additional educational materials! Thank You a lot!

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1126 - Applied Machine Learning in Python 的 1150 个评论（共 1,574 个）

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Dec 23, 2023

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Jan 1, 2023

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Mar 13, 2022

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Jan 25, 2022

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Nov 9, 2021

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Nov 4, 2021

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Oct 30, 2021

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Oct 28, 2021

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Oct 4, 2021

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Aug 12, 2021

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May 4, 2021

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Nov 4, 2020

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Oct 30, 2020

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Dec 26, 2019

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Oct 7, 2018

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Jul 2, 2017

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