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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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13,543 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

SS

May 18, 2020

The course was very informative but I face a lot of problems in installing Graphlab and Turicreate. I request the Mentors please use the Pandas data frame in place of SFrame. The mentors are cool.

MK

Jul 20, 2019

A great course, really designed to understand the underlying core concepts of machine learning using real-life examples which takes you through all that with little to no programming skills required!

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1901 - Machine Learning Foundations: A Case Study Approach 的 1925 个评论（共 3,159 个）

创建者 Muhammad H S

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Aug 7, 2019

good experience

创建者 Chris F

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Jan 10, 2017

Great course!!!

创建者 ROHIT

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Dec 21, 2016

Nice Experience

创建者 Sridhar C

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Oct 22, 2016

Really good one

创建者 欧阳登

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Sep 24, 2016

非常不错的课程，适合初学者入门

创建者 Tamir Z

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Mar 7, 2016

JUST AWESOME :D

创建者 Nitin S

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Feb 7, 2016

Awesome course!

创建者 Greg B

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Feb 6, 2016

Great overview!

创建者 Pavan K K

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Feb 1, 2022

It was amazing

创建者 Abdul B

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Sep 3, 2021

Good lectures

创建者 NITHIN P

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May 19, 2021

It helped lot.

创建者 Ruwanthi M

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Nov 27, 2020

A best course.

创建者 Md R I

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Nov 11, 2020

Awesome course

创建者 PRATHEEKSHA. P K

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Oct 26, 2020

Nice. Loved it

创建者 K. j S

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Aug 22, 2020

well experence

创建者 Hewavitharanage S A G

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Jul 25, 2020

awesome course

创建者 Mr. S P K

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Apr 17, 2020

Simply Awesome

创建者 Odai M

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Sep 20, 2019

Extremely fun.

创建者 byeongwook.seo

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Oct 26, 2017

Great Lecture!

创建者 surajit d

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Aug 20, 2016

Awesome course

创建者 Bilal A S

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Jun 6, 2016

Very useful ,,

创建者 Andrew M

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Mar 29, 2016

Super awesome.

创建者 Van Q

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Mar 1, 2016

very practical

创建者 Lee S H

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Jan 25, 2016

it's very fun

创建者 Viet N

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Nov 25, 2015

Awesome course