Chevron Left
Back to Machine Learning Foundations: A Case Study Approach

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

4.6
stars
13,527 ratings

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! Emily 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.

Filter by:

1051 - 1075 of 3,156 Reviews for Machine Learning Foundations: A Case Study Approach

By Dhruv T

•

Jan 18, 2016

The teachers are very good. Makes the rest of the specialization even more exciting.

By Zhihong C

•

Oct 13, 2015

A very nice and effective approach. It makes the complicated problem easier to take!

By Santhosh K J

•

Jul 17, 2020

It was actually a good experience learning machine learning in this problematic way

By Manali T

•

May 27, 2020

Happy and satisfied with whatever I have learned and gain Knowledge in this course.

By Ben D

•

Oct 25, 2016

Very good overview of the type of thinking required to do Machine Learning on data.

By Pujin W

•

Oct 4, 2016

This course is fundamental to machine learning and helps me to grasp the idea of it

By Yihong C

•

Mar 27, 2016

a really interesting and efficient way to help students understand machine learning

By Deleted A

•

Dec 4, 2015

Excellent introdutory course. The only downside is the use of proprietary software.

By Favian D

•

May 28, 2020

This is the best online course I attended so far. It is easy to understand and fun

By Amoghavarsha B

•

Oct 28, 2019

Good course for ML beginners! Introduces us to the world of possibilities with ML.

By Kaybee K

•

Jan 25, 2017

As a totally new Jupytor learner, I've enjoyed this appetizer course fully enough.

By Raymond D

•

Dec 19, 2016

This Course really helped me to understand the basic concepts in Machine Learning.

By Bill F

•

Oct 1, 2016

Too much emphesis on graphlab. exercises should be prototyped and done in sklearn.

By Bakhtawar U R

•

Apr 4, 2016

A Great course. Professors are enthusiastic and well acknowledged about the topic.

By 王先生

•

Jan 9, 2016

This Course is really a great way to get knowledge of the simple machine learning.

By Alexander P

•

Jul 21, 2021

Excelente que pasaran de Graphlab a Turicreate, dado que este ultimo es gratuito.

By Kishore K

•

Jul 13, 2020

The course was really interesting and drills into the basics of machine learning.

By YASHKUMAR R T

•

Mar 24, 2019

Best course to understand all the fundamentals of machine learning for beginners.

By Arul T

•

Sep 6, 2017

Excellent lessions to start with..Gifted to be part of these sessions.Very nice..

By Andrey U

•

Mar 10, 2016

Really great approach with a lot of useful and easy to get info. Really loved it!

By Nickil M

•

Nov 6, 2015

Enjoyed thoroughly.Nice way of implementing various tasks using ipython notebook.

By Caleb B

•

Dec 15, 2020

Great material and instruction. Very practical application as well. Well done!

By Adel M

•

Aug 29, 2020

this course is great for an introduction to ML, and the approach used is amazing

By Jeevanantham C

•

Jun 14, 2020

Good learned basic case study approach of various sectors using machine learning

By Kuan-Wen C

•

Jul 21, 2019

high level overview of ML. Good for people who want to know about ML intuitively