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Machine Learning Foundations: A Case Study Approach に戻る

ワシントン大学(University of Washington) による Machine Learning Foundations: A Case Study Approach の受講者のレビューおよびフィードバック



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....



Aug 19, 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.\n\nThe forums and discussions were really useful and helpful while doing the assignments.


Oct 17, 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


Machine Learning Foundations: A Case Study Approach: 2551 - 2575 / 2,657 レビュー

by Serban C S

Feb 11, 2018

Using a proprietary library for a paid course is not really a big issue but some people will be turned off by it.

by Pēteris K

Sep 23, 2017

Definitely a good intro to the richness of ML, but would have preferred more rigorous assignments and evaluation.

by Luca

Nov 10, 2016

not using scikit and assigment way too easy, not challenging, but high quality video, very easy to understand .

by Pubudu W

Jul 10, 2017

Good survey course on ML techniques. Not very detailed and the exercises are too simplistic for real learning.

by Nguyễn T T

Oct 14, 2015

the lectures are pretty great, engaging. the assignments stick with the lab exercise. the forum pretty active.

by Nebiyou T

Jun 08, 2017

Some of the modules lacked polish and have not been updated since initial recording!

But they were practical.

by Thomas M G

Feb 21, 2018

In my view, too much focus on GraphLab.

This is a problem because GraphLab doesn't seem to be open source.

by Zizhen W

Oct 17, 2016

Some instructions of the programming assignments are not all that clear, which wasted me a lot of time.

by Rajdeep G

Sep 07, 2020

They should upgrade the course in respect to python 3. Irrespective of that the theory part was great

by adam h

Feb 08, 2016

would vastly prefer if this was taught using sckit-learn and pandas, given their broader use.

by Cameron B

Apr 20, 2016

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

by Uday k

May 01, 2017

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

by Alexander B

Nov 04, 2015

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

by Naveen M N S

Feb 07, 2016

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

by Saket D

Feb 28, 2018

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

by kaushik g

Mar 25, 2018

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

by amin s

May 29, 2019

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

by Rajiv K

Jun 20, 2020

Have to improve for other environment.

have to explain other alternative too.

by Vamshi S G

Jun 28, 2020

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

by Julien F

Nov 17, 2017

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

by Marco M

Dec 04, 2015

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

by Pawan K S

May 15, 2016

Nice introductory course but too much dependence on graphLab create

by Jesse W

Dec 24, 2016

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

by Tushar k

Dec 01, 2015

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

by Konstantinos L

Jan 08, 2018

Nice course but too easy. Assignments should be more difficult