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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

4.6
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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

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：

2601 - Machine Learning Foundations: A Case Study Approach 的 2625 个评论（共 3,157 个）

创建者 Kevin A

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Jun 28, 2017

I haven't yet finish this course but is a excellent introduction for begin to study in this computer science field

创建者 ANKIT S

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Jul 5, 2020

i would rather give 5 but as i wasted lot of time due to graphlab so i have take one star for that my satisfaction

创建者 Sarra Z

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Jan 22, 2020

I liked this course, it is really based on study cases approaches and covers many problematics in machine learning

创建者 Ankit T

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Aug 29, 2019

The course provided by Course era was really very good. I want to thank to Course era to give me this opportunity.

创建者 Chris

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Jun 28, 2021

Very nice teachers and good overall knowledge. However the discusion forum is dead and learning materials are old

创建者 Bhaumik C

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Jul 16, 2020

Faculties were too good and explained very nicely. And I would recommend others this course for machine learning.

创建者 golap h

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Jul 2, 2020

This machine learning course is really effective for beginners.I have learned many basic topics from this course.

创建者 weiyuan x

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Apr 12, 2017

Good start course. It seems some information not covered so sometimes it is difficult to understand the content.

创建者 Christophe M

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Nov 8, 2015

Very didatic approach to machine learning. Easy access and still powerful technique to understand how it works.

创建者 Bhaargavi A

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May 8, 2018

Good Course. Teachers have taught it well and the jupyter notebooks are good and give a good deal of practise.

创建者 Soham K

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May 28, 2020

Overall it has been a great experience. But in my opinion, the course videos should be updated to TuriCreate.

创建者 Kuldeep K

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May 11, 2020

Kindly change the GraphLab package system, the majority of the compiler doesn't support this.

Else it was good

创建者 Azhar B T

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Sep 16, 2019

the course is good but rely on graphlab and lack of hands on with python is the reason i cannot give 5 stars.

创建者 Philip L

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Nov 12, 2016

Some quiz questions' answers are incorrect and instructors need to update the quit to reflect correct answers

创建者 Islam M

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Apr 19, 2016

-great introduction .

-go through a lot of exciting topics.

-but the implementation part is boring something.

创建者 M.sakif m

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Nov 16, 2015

Interesting class, but should have used open source python libraries instead of restricted license libraries.

创建者 Sheriff M

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Sep 15, 2019

Amazing they guide me help .Special sir working on project .It clear my concept with the real world example.

创建者 Vasudev V

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Feb 19, 2017

It would be great if you could intersperse theory and practical sessions. Otherwise, a very useful course...

创建者 Mohit P

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Mar 12, 2019

This course is a great starting point who has no earlier experience of ML. . Cheers to the course makers!!!

创建者 Yuting S

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Feb 26, 2019

Wonderful course.

The only problem is that I can't review the course materials after completing the course.

创建者 C K S

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May 27, 2018

Course was nice and especially special thanks to both the faculty's who make us to understand the course.

创建者 Benson C

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Sep 4, 2017

Interesting, I never used graphlab before. It would be better if this course went through algorithm deeper.

创建者 Javier M

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Jul 17, 2017

Great introduction to the topic. I think the juniper notebook is still buggy. Its stability can be improved

创建者 Sergio A M

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May 14, 2016

It is a great introduction but this could be done by adding a week more in each of the following courses.

创建者 Veera R

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Mar 11, 2016

Case study approach followed in this courser is very use full and helps to understand the methods better.