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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
8,061 个评分

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

AS

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.

FL

Oct 13, 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!!

筛选依据:

776 - Applied Machine Learning in Python 的 800 个评论(共 1,467 个)

创建者 EDWARD M

Oct 4, 2021

Great content ,, Greater instructor

创建者 Javad K

Mar 24, 2021

This course was very useful for me.

创建者 David W

Jan 12, 2020

A good introduction to Scikit learn

创建者 Navid A E

Oct 16, 2018

Absolutely the best professor ever!

创建者 Darren

Jul 2, 2017

Very Impressive and illustrative !!

创建者 Catherine L

May 16, 2020

Excellent course. I learned A Lot.

创建者 RICARDO D

Dec 3, 2019

Excellent material for intro to ML

创建者 Daniel H

Jan 4, 2019

Kevyn Collins-Thompson is a legend

创建者 Syam P N

Dec 17, 2018

Excellent course. Was very helpful

创建者 Sudhir T

Aug 1, 2018

nice course and easy to understand

创建者 Armand L

Apr 24, 2018

Very Good Course ! learned a lot !

创建者 Oleg D

Mar 24, 2018

ONE OF THE BEST THAT ONE CAN FIND!

创建者 Cezariusz P

Aug 11, 2022

Good materials but not easy exams

创建者 Prajay Y

Jan 11, 2022

Excellent well structured course

创建者 Natalia D P

Nov 5, 2021

LITLE BIT HARD BUT THE UI IS GOOD

创建者 BIBI I 2

Oct 31, 2021

Great course. Keep it up coursera

创建者 NITHISH K

Oct 11, 2020

Very excellent information gained

创建者 Deekshith N

Jul 22, 2020

Very good and interesting course.

创建者 Chanaka S

Jul 21, 2020

The hardest assigment i ever done

创建者 Ovi S

May 4, 2020

Awesome for intermediate learners

创建者 Himanshu R

Apr 27, 2020

It was great learning experience.

创建者 Xiaoming Z

Jan 11, 2019

Very informative, useful practice

创建者 Hemalatha N

Oct 24, 2017

Very informative & highly useful.

创建者 Fernanda R L

Oct 9, 2017

Very good, beyond my expectations

创建者 Eunjae J

Jul 1, 2017

It was really hard, but worth it!