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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
8,017 個評分
1,463 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:

776 - Applied Machine Learning in Python 的 800 個評論(共 1,454 個)

創建者 RICARDO D

2019年12月3日

Excellent material for intro to ML

創建者 Daniel H

2019年1月4日

Kevyn Collins-Thompson is a legend

創建者 Syam P N

2018年12月17日

Excellent course. Was very helpful

創建者 Sudhir T

2018年8月1日

nice course and easy to understand

創建者 Armand L

2018年4月24日

Very Good Course ! learned a lot !

創建者 Oleg D

2018年3月24日

ONE OF THE BEST THAT ONE CAN FIND!

創建者 Prajay Y

2022年1月11日

Excellent well structured course

創建者 Natalia D P

2021年11月5日

LITLE BIT HARD BUT THE UI IS GOOD

創建者 BIBI I 2

2021年10月31日

Great course. Keep it up coursera

創建者 NITHISH K

2020年10月11日

Very excellent information gained

創建者 Deekshith N

2020年7月22日

Very good and interesting course.

創建者 Chanaka S

2020年7月21日

The hardest assigment i ever done

創建者 Ovi S

2020年5月4日

Awesome for intermediate learners

創建者 Himanshu R

2020年4月27日

It was great learning experience.

創建者 Xiaoming Z

2019年1月11日

Very informative, useful practice

創建者 Hemalatha N

2017年10月24日

Very informative & highly useful.

創建者 Fernanda R L

2017年10月9日

Very good, beyond my expectations

創建者 Eunjae J

2017年7月1日

It was really hard, but worth it!

創建者 Deni M

2021年5月30日

G​reat course highly recommended

創建者 Dinith M

2020年11月19日

Learned a lot, excellent content

創建者 Gaurav R

2020年10月21日

best course for machine learning

創建者 Saurabh D

2020年5月24日

Very hands on. I learnt so much.

創建者 Priscila L

2018年11月10日

nice teacher, interesting course

創建者 Sayan G

2018年6月15日

Exhaustive and in depth coverage

創建者 Guido L

2018年2月8日

Very good, comprehensive course!