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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

PM

Aug 18, 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.

The forums and discussions were really useful and helpful while doing the assignments.

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.

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2401 - 2425 of 3,115 Reviews for Machine Learning Foundations: A Case Study Approach

By Zafer C

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Apr 30, 2016

Thank you very much for the course. I have developed my algorithm on Matlab environment, so far. I have introduced to Python with the help of the course and have obtained much crucial information about machine learning.

By vivek n

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Nov 27, 2016

Great course for beginners in machine learning.It has created a lot of interest in the field and looking with great expectations for the upcoming implementation courses for the inbuilt functions we used in this course.

By Brandon W

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Apr 1, 2020

Really good foundation for understanding Machine Learning and how to apply the specific methods. The approach of utilizing use cases makes it easier for the user to paint the picture and understand the business problem

By Vishwam

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Mar 28, 2016

It was overview of things that one does using ML. The instructors focused just on the concepts, leaving the internal working on algorithms and that helps someone is very new to this field to first get a broader picture

By Aditi S

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Jul 27, 2020

Nice course for fundamentals with good assignments which really make us work. Only bad thing is that the material was somewhat outdated and they haven't updated all the things like the quizzes with the new framework

By Aditya N P

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Jul 13, 2020

It's a really good course and it gives a basic intoduction to ML and its fundamentals.

The course just need to be updated, many things are of older versions & it creates a lot of dificulties in completing the course.

By Jesper W

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Apr 12, 2016

Great hands-on and practical introduction to graphlab and python that is pretty useful in the later courses. I great way of starting a specialization with an overview and small samples of what we are going to learn.

By Khiem N L H

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Sep 8, 2019

Provide high level overview of main Machine learning concepts. With real life cases, it makes it easier to understand the concept.

The course do not include specific technical but rather a high level introduction.

By joseph b

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Jul 30, 2017

Pretty good introduction. Enthusiastic teachers give you a good overview with a fair sprinkling of details - enough to get wet your appetite and the language to explore the concepts outside of the course material.

By Ramesh M

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Sep 27, 2015

It is giving me inspiration and clarity what we can do if we know machine learning , its clearing clouds to see the sky and letting me to reach it . Thankful to the instructors and to the coursera. love open stuff

By Francois-Xavier

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Nov 24, 2016

Very simple black box approach to ML. Pretty much anyone can start and play around with ML following this course.

I would have like to get a bit more into details but this is great introduction course for anyone.

By Maitree M

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May 31, 2017

While the black box approach makes it easy to understand and grasp at a use case level, I missed some of the intuition associated with how these algos get the work done. Overall, a good beginner course to take!

By Dmitri B

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Mar 8, 2017

It has made a lot of fun, only what I didn't like, that in this course GraphLab is used. We do not use it in my company, so I would prefer to use Open Source Software packages and not one what has to be bought.

By Corkine

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May 24, 2018

This course is awesome, and it fully integrates with actual needs and theories. However, I feel that the theory is somewhat lacking. It is possible that the teacher will talk more about the rest of the course.

By Bálint K

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Feb 14, 2017

love the approach, although the total lack of support (on the forums) is a bit discouraging. also, there are some errors that make it hard to understand the last week's material, but other than that, it's ok.

By HIMANI B

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Jun 18, 2020

IT is a good course to start your machine learning journey. It could have been better with more popular libraries like sklearn and pandas. But the course material is very understandable and nicely delivered.

By Maxence L

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Aug 10, 2016

Une très bonne introduction par la pratique aux différentes notions et concepts du Machine Learning, avec assez d'éléments concrets pour pouvoir commencer à mobiliser ces théories dans un contexte pratique.

By Danielle S

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Dec 7, 2015

+ Excellent video lectures.

+ Good overview of the field.

+ Nice working examples with good instruction video's.

-- No help with the practical assignments although the Python examples given are not flawless.

By Yannan C

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Sep 10, 2021

Four years ago, this course deserves a five-star review. But as the turicreate has changed a lot, some functions cannot be used and some error appears in the hand-on part. But, it is still pretty good.

By Deepak M

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Apr 2, 2017

The Course was very neatly presented, although we used lots of predefined functions to work around Machine Learning Algorithms it was good to know about the concepts that was thought extremely well.

By Rodrigo d A M

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Feb 3, 2022

I was very disappointed with the exclusion of the final courses and the capstone project. The most interesting part of specialization no longer exists and no plausible justification has been given.

By Sunil K S

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May 19, 2020

The course was very informative but I face a lot of problems in installing Graphlab and Turicreate. I request the Mentors please use the Pandas data frame in place of SFrame. The mentors are cool.

By Hanz C V

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May 21, 2016

Good for a introductory course if someone is getting started with machine learning, but as part of an specialization i think is useless (for people who are planning to take all the specialization).

By Jayakrishnan M M

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May 26, 2020

Graphlab is used during the class, where as in assignments, turicreate is used. This causes slight variation in the results between the two. This may cause loss of points in the assignment.

By Najamuddin B

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Jun 1, 2017

Course contents are good - however the forums are not active and there is no follow up from faculty to update the course specialization following the change in course structure (eg. no capstone)