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返回到 Machine Learning Foundations: A Case Study Approach

学生对 University of Washington 提供的 Machine Learning Foundations: A Case Study Approach 的评价和反馈

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13,532 个评分

课程概述

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....

热门审阅

BL

Oct 16, 2016

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

Dec 19, 2016

Great course! Emily 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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2851 - Machine Learning Foundations: A Case Study Approach 的 2875 个评论（共 3,157 个）

创建者 shane

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Oct 22, 2015

Very practical.

创建者 Rohit K S

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Sep 30, 2020

Good Course!!

创建者 Divyashree

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Sep 13, 2020

A good course

创建者 SHAHID S

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May 15, 2022

Nice Content

创建者 ANURAG Y

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Nov 30, 2021

good teacher

创建者 Rupali G

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Nov 1, 2017

good content

创建者 Andre G

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May 13, 2016

Good course.

创建者 廖敏宏

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Sep 24, 2020

Very useful

创建者 P.BHUVANASHREE

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Sep 18, 2020

interesting

创建者 HASNA V N

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Jul 19, 2020

Good course

创建者 Shubham D

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Dec 3, 2016

nice course

创建者 Le H P

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Aug 16, 2019

well done!

创建者 Daniel Ø

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Jan 18, 2016

very basic

创建者 Muhammad A K

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Nov 27, 2020

very good

创建者 Sayam N

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Sep 25, 2020

Excellent

创建者 Aishwarya S

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Jul 5, 2020

very nice

创建者 Zhen W

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Jul 5, 2017

Good ~~~~

创建者 Kevin C N

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Dec 10, 2016

Thanks!!!

创建者 Oriol P

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Mar 30, 2016

Was nice!

创建者 Sreemannarayana B

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Feb 23, 2016

Excellent

创建者 Oumar D

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Feb 21, 2016

Efficient

创建者 DEBASISH M

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Sep 20, 2020

Like it.

创建者 John M

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Jul 4, 2018

Liked it

创建者 Evan Y

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Dec 23, 2018

So good

创建者 Abhishek P

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Jul 25, 2023

GOOD