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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
12,075 個評分
2,892 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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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.

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

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2526 - 机器学习基础:案例研究 的 2550 個評論(共 2,804 個)

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

創建者 Poornima S

2019年2月18日

It is designed really good.

創建者 Hyeong R J

2017年2月2日

Good lecture and practices.

創建者 Marcos M M

2017年8月24日

Great introductory course!

創建者 SUPRIYA V S

2018年6月30日

Nice course for beginners

創建者 Vinicius G d O

2016年6月23日

Good introductory course.

創建者 José T G R

2015年11月1日

Very good!!! Excellent!!!

創建者 Tushar A

2020年7月13日

This is a nice course..

創建者 Fernando S

2017年8月20日

Easy going, very good!!

創建者 Godwin

2017年6月4日

Very interesting :) WOW

創建者 Annie I R

2016年1月4日

This is a great course.

創建者 Mayur S

2017年1月18日

its good, if new to ML

創建者 Shikhar S

2020年12月8日

Great course to start

創建者 Wridheeman B

2020年6月30日

It was a great course

創建者 Eric S

2016年1月5日

Pretty good, overall.

創建者 Mahajan P J

2019年12月26日

The course was good.

創建者 Richik G

2019年7月11日

computer vision best

創建者 Pieterjan C

2017年10月2日

very useful to start

創建者 Shreeti S

2017年8月16日

Good to start with.

創建者 Waquar R

2016年8月8日

this is really good

創建者 Vivek A

2016年4月18日

Enjoyed this class.

創建者 Fei F

2015年12月22日

Easy for beginners.