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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.

筛选依据：

2826 - Machine Learning Foundations: A Case Study Approach 的 2850 个评论（共 3,157 个）

创建者 Fernando S

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Aug 20, 2017

Easy going, very good!!

创建者 Godwin

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Jun 4, 2017

Very interesting :) WOW

创建者 Dr. A I

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Jan 3, 2016

This is a great course.

创建者 Mayur S

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Jan 18, 2017

its good, if new to ML

创建者 Shikhar S

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Dec 8, 2020

Great course to start

创建者 Wridheeman B

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Jun 30, 2020

It was a great course

创建者 Eric S

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Jan 5, 2016

Pretty good, overall.

创建者 Mahajan P J

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Dec 26, 2019

The course was good.

创建者 RICHIK G

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Jul 11, 2019

computer vision best

创建者 Pieterjan C

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Oct 2, 2017

very useful to start

创建者 Shreeti S

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Aug 16, 2017

Good to start with.

创建者 Waquar R

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Aug 8, 2016

this is really good

创建者 vivek a

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Apr 18, 2016

Enjoyed this class.

创建者 Fei F

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Dec 22, 2015

Easy for beginners.

创建者 TALHA J

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Aug 29, 2021

it helped me a lot

创建者 Joydev H

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Nov 15, 2019

Awesome Experience

创建者 Binil K

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Jan 10, 2016

Really great one!!

创建者 Hiếu N Q

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Dec 28, 2015

Good for ML newbie

创建者 amit d

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Feb 3, 2020

nice explaination

创建者 Arnab N

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Jan 4, 2020

Very nice program

创建者 Rahul S

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Dec 19, 2020

GREAT EXPERIANCE

创建者 SURUTHI T

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Jul 5, 2020

more informative

创建者 Oscar M

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May 29, 2016

Very insightfull

创建者 Tulasi P D

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Jul 14, 2020

it is so useful

创建者 Rohit K

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Apr 17, 2020

very intersting