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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

RH

Jun 8, 2017

I felt this course did a good job introducing the student to Machine Learning. The examples and hands on assignments brought the concepts home. I was able to use the knowledge immediately at work.

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!

筛选依据：

3001 - Machine Learning Foundations: A Case Study Approach 的 3025 个评论（共 3,159 个）

创建者 Reem N

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Jun 23, 2022

It is very general however it gave me an insight to different machine learning applications.

创建者 Cameron B

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Apr 20, 2016

The course is ok, the instruction was very poor for the deep learning section of the course.

创建者 Uday K

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May 1, 2017

The theories for the models should be explained in more detail and with few more examples.

创建者 Alexander B

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Nov 4, 2015

lectures were well done, but the strong focus on using graphlab ruined this course for me

创建者 Mehrave S

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Aug 7, 2024

turicreate made lots of problem for me, it was better to instruct it by another package

创建者 Naveen M N S

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Feb 7, 2016

Decent course. Not very satisfied with the assignments as they are suited for graphlab

创建者 Carlos A C L

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Jan 25, 2021

all lectures are obsoleta, and it's neccesary to install a WSL, the rest very well.

创建者 Saket D

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Feb 28, 2018

Would have been great if anything compatible with python 3 was used in the course.

创建者 Kaushik G

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Mar 25, 2018

Content was good but was few years old and things are pacing up a bit these days.

创建者 amin s

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May 29, 2019

primitive course, didn't expect this low standard from university of Washington

创建者 Rajiv K

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Jun 20, 2020

Have to improve for other environment.

have to explain other alternative too.

创建者 Vamshi S G

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Jun 27, 2020

i think the course should be updated, graphlab and some other are outdated.

创建者 Julien F

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Nov 16, 2017

Some quiz questions were vague and/or ambiguous, or not covered in talks.

创建者 Marco M

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Dec 4, 2015

Too much synthetic on very important parts, too much focused on graphlab

创建者 Alejandro V

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Nov 12, 2020

TuriCreate is not the apropriate tool for practical Machine Learning

创建者 Pawan K S

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May 15, 2016

Nice introductory course but too much dependence on graphLab create

创建者 Jesse W

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Dec 24, 2016

It is better if allow me upgrade only when I finished this course.

创建者 Tushar k

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Nov 30, 2015

Good course to begin machine learning with but it's too easy !!

创建者 Konstantinos L

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Jan 8, 2018

Nice course but too easy. Assignments should be more difficult

创建者 Felipe A S S

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Jan 23, 2021

The libraries used on the course are a little bit unsopported

创建者 Nadeem B

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Jul 27, 2021

Concepts and explanation is great but using outdated modules

创建者 ATHARV J

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Sep 14, 2020

The course should be taught in pandas rather than graphlab.

创建者 Max F

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Jan 10, 2016

Not a bad course, but extremely basic. Was expecting more.

创建者 Adrien L

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Feb 2, 2017

No good without the missing course and capstone projects

创建者 Aleksey C

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Dec 11, 2016

....mmm fsdfg gsgsd sgsdgsdg sdsdgsdg ggsgsd sgdsdgsg