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Learner Reviews & Feedback for Machine Learning Foundations: A Case Study Approach by University of Washington

4.6
stars
11,941 ratings
2,860 reviews

About the Course

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....

Top reviews

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!\n\nEmily 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.

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2651 - 2675 of 2,774 Reviews for Machine Learning Foundations: A Case Study Approach

By suresh k p

Jul 28, 2018

Nice explanation of basic ML but I would suggest please provide the practise tool with proper integration.That is a big headcahe in this course.

By Paul C

Nov 24, 2016

A solid course, let down by quality issues in the last two modules. I hope these are fixed soon because it would make this a top notch course...

By Jawahir M A K

Jul 17, 2020

It will give you an overview about the ML concept. But to get detail we need to have the specialization course or learn it our self.

By Kristoffer H

Jun 8, 2016

Get ready for a course that assumes you have all the software they use already installed without advanced notice or instructions!

By Abiodun M

Mar 18, 2018

Very good course; except the bugs in Graphlab with reference to .apply and lambda workers command . This needs to be fixed.....

By Corey K

Mar 11, 2016

All algorithms were black boxed. It was a nice course on how to use Dato's GraphLab and an overview of ML concepts.

By Michael B

Nov 2, 2015

Fun lectures but the coverage is too simplistic. Looking forward to the more in-depth courses in the specialization.

By Aleksei Z

Jan 16, 2020

Materials from video differ from the web ( in videos graphlab, in materials Turicreat), including home assignment.

By Ajay S

Mar 4, 2019

Good for beginner level, not for intermediate or advance level. I learned more about graphlab than anything else.

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 13, 2015

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

By ADNAN A G

Oct 9, 2020

old and bad quality but very good explanation half of the course is programming there is no machine learning.

By Nebiyou T

Jun 7, 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 16, 2016

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

By Rajdeep G

Sep 7, 2020

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

By adam h

Feb 8, 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 1, 2017

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

By Alexander B

Nov 4, 2015

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

By Naveen M N S

Feb 7, 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.