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返回到 机器学习基础:案例研究

學生對 华盛顿大学 提供的 机器学习基础:案例研究 的評價和反饋

4.6
12,287 個評分
2,946 條評論

課程概述

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python....

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BL
2016年10月16日

Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much

SZ
2016年12月19日

Great course!\n\nEmily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.

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2551 - 机器学习基础:案例研究 的 2575 個評論(共 2,861 個)

創建者 Alberto M B

2018年9月21日

Good overview but basic information.

創建者 Neilmani S

2018年2月13日

It was one of the amazing Course !!!

創建者 Yang W

2016年3月10日

A very practical course. Like Emily.

創建者 Priyanka J

2020年6月18日

Good work but need more explanation

創建者 Abhinav M

2018年4月1日

Great material to try out ourselves

創建者 Dominik S

2017年6月26日

Very nice. I think I learned a lot.

創建者 Sunny D

2020年5月3日

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創建者 Shishir S

2017年5月28日

Good course for the beginners ....

創建者 kulbhushan

2016年10月27日

Very informative and useful course

創建者 BIAN D

2016年6月15日

homework could be more complicated

創建者 Royal P

2020年11月18日

very knowlegdeful and interesting

創建者 Reginald A L

2020年9月20日

A good course to learn. Thank You

創建者 YuechuanLi

2017年10月15日

look this approach for beginners!

創建者 Yunqi H

2020年1月24日

Great intro class but very fun !

創建者 Nitin K

2017年5月4日

Pretty good introductory course.

創建者 Arish A

2016年6月8日

A good introductory level course

創建者 Mario G

2016年4月26日

Good for an introduction class.

創建者 Weiyi W

2018年6月11日

Quizs are harder than lessons.

創建者 Mehul P

2017年8月1日

Nicely explain use case of ML.

創建者 Jijo T

2015年10月6日

I love the hands on exercises.

創建者 Mazen A

2016年10月9日

the best introduction for ML.

創建者 Rishabh C

2020年7月23日

Awesome course to start with

創建者 Rakesh G

2019年4月15日

A good beginners guide to ML

創建者 RISHAB P H

2020年4月15日

add more practical's please

創建者 Mahesh B

2019年10月10日

Good start for ML beginners