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
13,374 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

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.

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

Filter by:

2376 - 2400 of 3,115 Reviews for Machine Learning Foundations: A Case Study Approach

By Bachir S

•

Dec 11, 2017

this is an excellent course , for those who really wants to learn machine learning . It has a good lectures and courses and programing assignement . but there one small program is that it uses graghlab create theat works only with 64 bit computer

By Xiaohua X

•

Feb 2, 2016

I would give 4.5 stars if I could. This is a very good introduction to the whole Machine Learning Specialization series given the topics covered, especially with practical cases. However, as a standalone course it lacks depth for a 5 star rating.

By Calin-Andrei B

•

Mar 22, 2018

Very good material with a lot of real-life example for having a high level intuition on basic machine learning algorithms. However, the assignments are not very challenging. I hope this will change during the next courses of the specialization.

By Will G

•

Jun 17, 2016

Really great intro to machine learning - I think this class would have been better if the programming assignments had been a bit more difficult, but overall really enjoyed this class and looking forward to continuing with the specialization.

By Christos Z

•

Dec 16, 2017

The course was interesting and the instructors seemed to care about the outcome. The issue on this course and this specialization in general is the use of their own private software instead of open source frameworks like pandas and sklearn.

By Jitin J

•

Jun 20, 2020

Great course to get started on the ML journey. I had some initial difficulty in setting up the environments and manage the various package dependencies. But once it was set up then the rest of the course was really enjoyable and enriching.

By Haiqing K

•

Nov 1, 2017

It is a nice course for me to review some data science concept and learn a little about deep learning. The use of Graphlab is a little annoying considering rarely any companies use it but the code used is easy to understand for beginners.

By Michael C

•

Apr 9, 2017

Good introduction to machine learning concepts and I'm looking forward to a deeper dive in later courses. Would have preferred the course use industry standard, open source tools, but GraphLabs/SFrames didn't cause an issue for me at all.

By José O C

•

Sep 5, 2019

Very well driven by the lecturers. Very good overview. My only concern is the difficulity in setting up the working environemnt for python.: I needed to solver serveral issues not mentioned in the guidelines, which took me several hours.

By Muhammad S A

•

Aug 31, 2019

Thoroughly enjoyed the case-study approach toward learning fundamental machine learning concepts. The abstraction level is a great match for professionals who are beginning to understand key machine learning concepts beyond buzz-words.

By Antonio A

•

Dec 17, 2015

Great course! I had to watch some of the videos over and over again for the material to really sink in but after I understood what's happening it was very enlightening. This course will really get you hooked into learning data science!

By Juan M O M

•

Mar 10, 2017

Very good introduction to machine learning. Concepts are well presented and explained, i would have preferred to use a Open Source library in the assignments, but is not bad to know how to use Graphlab, a very powerful tool, for sure!

By Sridhara S

•

Apr 17, 2018

Lab work instructions are not well written, it is taking lots of time to understand what we are trying to do. Next thing is we need to know the right answers after certain threshold so that we can understand where we are doing wrong.

By Fabrizio S

•

Feb 6, 2016

Very good approach to this topic. Very good explanation even of complex subjects. It is a general overview and allow the student to have a basic knowledge of all machine learnings sub-topics. Maybe to depended on dato dato framework.

By Alejandro G S

•

May 15, 2016

It's a practical approach to different machine learning techniques, it would be great not to depend a lot of DATO, but I think at the other hand that dependency allows to focus in machine learning than in program things with python.

By Soumen H

•

Jun 7, 2020

Best way of teaching such peculiar concepts with so ease using the best live examples . Great sessions and great energy of Carlos and Emily made it cool and much more simpler to learn . Had a great learning experience with them .

By Qi Z

•

Jun 24, 2018

Course is pretty well designed, I like how practical it is and the case study approach. It's a great introduction course to ML. One suggestion I have is to make it easier to use Sklearn and panda which are more popular in industry

By Dan G

•

Aug 4, 2017

First of all - you are both excellent teachers and concepts were very well explained at this foundation level. My only doubt was how current the content is given that ML and AI are evolving so much everyday. Otherwise - great job!

By Jhonatan J

•

Feb 1, 2016

The course its a pretty good introduction to machine learning, we explored different technics, models and alghoritms. Im very pleased of what I learned in this course. 4 Stars because some explications needs a little more detail.

By Daniel s

•

Feb 24, 2016

Could have explained a little more about what we were doing for some of the modules. For example, in week 6, I didn't really understand the 'deep_features' column or how the values for that column were generated for each picture.

By Srihari R

•

Jul 3, 2016

I have just completed the course.I have got an overview of how machine learning works.What I felt is that the course is a very good one for the beginners in ML.

PS: Increase the toughness and level of the questions in the quiz

By Kiren S

•

Mar 2, 2016

Awesome module, but the Deep Learning course should be redone in my honest opinion. The explanation for it is on way too high of a level compared to the other courses within this module and way too bogged down in the details.

By Gaurav K

•

Apr 9, 2021

It is a very awesome course but I am giving it a 4 star because I enrolled in it, without knowing that it is using Turicreate and teaching graphlab but in real life, there is barely any company which uses these two libraries

By Tal v D

•

Apr 17, 2016

I think the text of the programming assignments could be improved. The minimum that should be done, I think, is to do some serious proofreading. I found a lot of mistakes in language (and English is not my native language).

By Sriram R

•

Aug 1, 2017

It'd have been better if at least one of the case studies started with raw data. The steps to get raw data to Graphlab/SFrame understandable form are not covered and I think it is important. Otherwise an excellent course.