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

4.6
stars
9,288 ratings
2,217 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 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

SZ

Dec 20, 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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176 - 200 of 2,136 Reviews for Machine Learning Foundations: A Case Study Approach

By Layne C

Oct 30, 2015

This is a very good introduction to ML. I felt that everything was presented in a very straight forward manner. A little more guidance on installing python and jupyter would be beneficial for those that have not used python packages much.

Overall a great course and I am looking forward to the more in depth courses :)

By Scott v K

Sep 26, 2015

Great overview and engaging introduction to regression, clustering, classification, and deep machine learning with hands-on ability to see some of these practices in action programming exercises in Python. Good introduction to the more in-depth materials which will be covered in other courses in the specialization.

By Zheng L

Oct 24, 2019

This course is very interesting and teaches you the basic concepts and practice applications of machine learning technics. The only drawback is that this course rely heavily on graphlab package which cannot be used in Python 3.7. Took a long time to search for alternatives in sklearn instead to finish assignments.

By Adil A

Oct 13, 2016

This is an excellent course... One can tell that a lot of effort went into making this course fun and easy to work with... Almost certainly the most fun to work on course I've taken on Coursera so far... The instructors are very nice, the video lectures are fun and the assignments are easy and fun to work with...

By Fakrudeen A A

Aug 05, 2018

Excellent course and highly recommended - covers fundamentals, TF-IDF, cosine. jaccardian similarities, recommender systems (precision/recall, AUC), deep learning via transfer learning (not having to explicitly build a model for the problem).

Exercises could be done in some tool which is common across industry.

By Bola M

Jul 19, 2016

Awesome course! Only gives an introduction into the Machine Learning topics but does it well. As a Technical PM in the software industry, this was enough depth for me to understand the basics of machine learning algorithms. Also has good hands-on tutorials with Python to implement the algorithms which is great.

By Jorge H

Nov 07, 2016

Excellent course!!... It has been the best online course so far. I really enjoyed the Use Case approach, and got really excited with the fact that –although being an introductory course- I got really a good intuition and hands-on experience about use of machine learning for real applications.

Congratulations!!

By Carol V

Feb 27, 2017

This course helped me develop a good understanding of complex machine learning concepts.

The tools were easy to use and helped me learn quickly. Unlike other programming classes I've tried in Coursera, I did not have to deal with programming environment related problems. I learnt important python skills also.

By Baranitharan S

Apr 14, 2018

The course sets a strong foundation for someone who wish to specialise in the AI and ML space. The course content is easy for a beginner with a very little or no (you gotta believe it) software coding background. The instructor are awesome and help you to go thru the course with ease and not getting bored.

By Srividya N

Nov 01, 2017

There is so much of flexibility. It is so cool and so interesting... I could complete this complex course so easily with some of the key activities like below:

Exercise videos

taking quiz questions multiple times with no penalty

simple English and explanation of complex information in simple and easy terms

By Walid O

Mar 04, 2017

this is course is very good for a beginner who wants to know what is machine learning , why we want this , what is its application .

also you will understand many algorithms used to manipulate data to do very cool applications and you will do this yourself .

they made it very easy to understand , thank you .

By Xiangwei C

Jul 09, 2016

It is a very well structured and effective course. I really learned a lot of machine learning techniques that I can use immediately. Both instructors did great job explaining the concepts and algorithms. Very powerful python tools are introduced, and I love them! Definitely worth the money that I paid for!

By Chengyu H

Sep 16, 2016

It is a good introduction to machine learning with cases. It explains all the big concepts in a high level, and uses all the out of box functions of graphlab to implement those ideas. Do not expect to have super detailed understanding of all the algeralisms and step by step how to do it from scratch.

By Stephen M

Dec 13, 2017

Great SURVEY of use cases and methods in machine learning and an opportunity to familiarize yourself with Jupyter notebooks, Python and GraphLab Create. This is an orientation to machine learning; none of the use cases or methods are covered in great depth (that comes in the courses that follow)

By Deepak

Aug 24, 2016

This course gives overview of what we are going o learn ahead in machine learning course. Carlos and Emily they both explain stuffs in very detail manner. IN fact it so much fun to learn when you understan thing and specially these cool stuff i hope to see some more courses on this in future. :)

By 邵帅

Dec 05, 2017

I think this course is a quite cool fundamental Introduction. After finishing the course, you can do real things like building a MPC (Model Prediction Control) system using regression technique and so on. I fully encourage you guys joint this course for a getting started step into the ML field.

By Aislyn N R

Aug 26, 2016

Very well presented! The tools are explained and provided in a way for genuine learning and application. The jupyter notebook assignments allow one to jump into the material, while taking detailed notes along the learning 'journey'. I am excited for all the other courses in this specialization!

By Partha P M

Dec 20, 2016

Learned iPython Notebook which is good for Machine Learning.Helped me to understand the basics of all the ML techniques and helped me understand where to apply which ML model. This course will not teach u ML in depth , for in depth knowledge u have to take other courses in the specialisation.

By Yogeshwar G

Apr 28, 2017

It was amazing! I cannot describe the feelings one experiences when playing with the machine learning codes. My only complaint is that I would have wanted more in the neural network/deep learning module, but I guess there will be another specialization course for that. Thank you Professors!

By Willismar M C

Aug 31, 2016

It was a great course, showing the general applications of machine learning and great tutorial in how to implement the solutions without the technical and theoretical part of it. Also the use of IPhyton give a lot of flexibility to the material and the exercises. I really enjoyed. Thank you

By Anindya S

Jan 02, 2016

Dr. Carlos Guestrin and Dr. Emily Fox are amazing. Needless to say, their way of teaching is absolutely brilliant and fun to learn, concepts which took me few days to learn now takes an hour or so, this is primarily due to their mastery on the subject matter and their lucid way of teaching.

By Roberto C

Oct 22, 2015

I really liked this course (I have one week to finish, but I have enough data to judge). The professors are really pedagogic and the examples are really clear. The only thing is that I would rather have more difficult programs, I feel like that I do not have much to think to pass the tests.

By Nguyen D P

Jun 24, 2017

I really love this course. After the first course i get the basic background machine learning. I can see everything that machine learning can apply. The course just easy to understand and give me one years trial graphlab lib for python which is useful for me can access machine learning .

By Abhijit P

Jul 17, 2017

Excellent course to get you started with machine learning. Both the presenters bring a in lot of their expertise and experience to make this course fun and engaging. Lot of examples are shared which help to understand the topics in a much better way. This training is really top notch. I

By Wan S L

Jun 12, 2016

One of the best courses I have learnt from Coursera. I love the way the lectures are presented, and how great our teachers guide us through it! I love the materials given, the projects and assignments, and the fun part interacting with teacher. Overall, five stars! Highly recommented!