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

13,226 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


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.


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

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176 - 200 of 3,070 Reviews for Machine Learning Foundations: A Case Study Approach

By Rohail K

Jan 9, 2018

Amazing course on Machine Learning.I have tried other courses on Machine Learning but none has made it so simple for me as this course.I started other courses but at some point I was stuck but this course explains all concepts so easily and gradually .Highly recommended for anyone who want to start learning machine learning.Even if you do not have programming experience, its easy to follow.

I congratulate both the instructors Emily and Carlos for making this brilliant course.

My most favorite part of this course is when Emily is trying to pronounce the name "Pele" and Carlos corrects here lol.

By Siva J

Feb 22, 2016

A very well designed course. This course clearly sets a path for introducing basics and fundamentals in a way that makes it challenging but insightful. It establishes the purpose for more advanced courses in this specialization and the need to dive deep in this fascinating subject (machine learning).

After completing this course alone, I can say categorically, there is no turning back, there is no sitting on the sidelines, there is no learning something peripheral. Doing this entire specialization will make any successful learner a very successful contributor in the world of Machine Learning

By Yanan L

Jun 1, 2016

A little touch on the basic concepts and a detailed step by step tutorial on applying ML using python packages, this could serve as a good complement to the beloved Stanford ML course on Coursera. For someone who is just getting started with Python, I think this course would be significantly more useful if the packages used are pandas and scikit-learn, since those are more heavily adopted in the industry. The course is well organized and the programming examples are fun. This is a great introduction course to ML and I do expect highly of the subsequent courses in this specialization.

By Arif A

Mar 24, 2019

I have a fairly good background in mathematics and have read through major parts of the Deep Learning Textbook by Goodfellow et. al. One year later I wanted to revise ML again. People who are complaining that there is no mathematical or algorithmic rigor in this course need to understand that this is meant to be an introductory course in order to pique interest in the learner and drive him/her to pursue this field further. Heavy focus on math and algorithms straightaway does not work for most people. Hence, I conclude that this is a good intro course which does it's job quite well.

By Kowndinya V

Mar 4, 2018

This course provide a very intuitive explanation of different machine learning models. It also has a good blend of hands on programming. Especially the combination of python notebook and graphlab give a unique experience. The visualizations from graphlab are amazingly good. It's really additive.

I would like to Thank Emily and Carlson for their great work in putting the right level of content for this course keeping the audience in mind. I feel bottom of my heart that I could really learn something significant and meaningful.

Overall, I must say it was an awesome experience.

By Pratyush K S

Oct 13, 2019

Machine Learning is here to stay. Period.Course content was awesome, gave me lot of insights. The content are very well versed, assignments and quiz are quite challenging and good.

There was a impressive focus on the basics and fundamentals of each topic.

Great overview, enough details to have a good understanding of why the techniques work well in real sectors(especially retail,healthcare,financial services,etc).

Finally I would like to thank the LKM Team of Accenture for giving me the wonderful opportunity to learn this course and upskilling myself.



By Oren P

Nov 1, 2015

I love the case-based method and the focus on the practitioner. Almost every other competing course and textbook puts too much focus on the mathematics proofs, on the nuts and bolts of the algorithms. I also like the informal attitude of the instructors.

The dependence on Dato, and the fact that Dato does not have a student or home use pricing are bad. After learning all of this, am I supposed to buy software that costs US$ 400 per month (matlab has a student edition for a one time price of $90)

Continuem o excelente e inspirador trabalho! Obrigado Carlos & Emily!

By Sandesh K

Jun 4, 2020

This is an amazing course. I completed my beginner's course in python using Microsoft's resources that were made available on youtube. After that, I took this course, it needs a good amount of patience and some basic knowledge of python to understand the lectures. As you move ahead in the course, the professors make you feel really comfortable with the theories followed by a quiz on theory and then a practice assignment followed by a quiz to check our understanding of the subject matter.

The six weeks of hard work will surely be useful for the rest of my career.

By Gilles D

Apr 20, 2016

Good overview and introduction to the more detail content of the following courses. If you are not familiar with Python, this will ease you into the language and enable you to follow.

There is a certain style of teaching that you need to get accustomed too in the beginning but when it is done, lessons become very clear and easy to follow. Moreover, it becomes "so what is happening next?" and you are looking forward to the next lesson.

Again, this is mainly an overview (with content) and a lot of the material will be reviewed more in detail afterwards.

By Khaled E

Dec 2, 2015

I really enjoyed taking this course in Machine Learning. It is my first course in machine learning. The instructors are really great. I like the course logistics, and how it builds up the foundations of the critical thinking in machine learning, rather than learning specific tools. I am really looking forward to complete this specialization with the Capstone project.

I know how hard it can be to prepare an entire specialization like this. I appreciate the time and the effort the instructors have put to make this specialization happen and see light.

By Young S S

Feb 8, 2016

This is an incredibly surprising course to me. Learning an up-to-date ipython notebook along with carefully designed instructions helped me have a better understanding of what machine learning is about and how it can be approached. Even though the last part was somewhat challenging to me, I learned a lot from this course and more than anything else, I could have some sort of vision in machine learning. I would like to keep working on machine learning specialization. Thank you for your warmhearted and incredible instructions! Thank you.

By Vaibhav B

Apr 30, 2016

This is an excellent course for the starters to get holistic insight on the niche world of Machine Learning.

It is also a revisit to the notebook where you will spent time in evaluating truth tables and drawing planes to derive answer for assessment questions, a refreshing change from the regular work.

I am sure in-detail sessions/courses following this foundation course would definitely be of great learning and look forward to be part of those sessions and get enlightened on the new disruptive technological milestones.



By Tony M

Aug 30, 2016

Fantastic course. A great, high-level, and gentle introduction to the most important machine learning techniques in use. The professors also co-own a market-leading machine learning company that produces a tool for machine learning practioners and data scientists called GraphLab Create. The tool itself is also fantastic as it not only creates and manages the environment for the Python notebooks and neatly installs Anaconda for you, it takes the guess work out of applying some of the more sophisticated machine learning models.

By Jason J

Feb 14, 2018

I lost a week getting access to the course materials. Using the coursera iPython notebook did not work because of issues with the GraphLab key you have to individually obtain. Still I have to give this class 5 stars. Because, after that large hiccup, the material is fantastic. Emily is a great teacher and walks you by the hand through all the material. Sometimes I have to watch the videos twice, taking lots of notes, but if you put in the work, you will have a real intuitive understanding of the course material.

By Jesse G

Jun 24, 2016

"Breadth with Depth!" That's what you'll get from taking this course (quoted because those are the words used to describe the general education pattern at my university). I never used the forums for this course and had to learn software than what I'm used to working with (sci-kit). When stuff worked it was rewarding, but other than that, expect to read documentation if you get stuck. Finally, for anyone who want to know what Machine Learning is about, this courses is the sampler of what is to come in later courses.

By Christopher A

Oct 17, 2015

I really liked the case study approach to the topic. The instructors' approach to teaching through the Python notebook made it easy to follow and see things implemented as you learned them. In addition, they presented the material at a good level - not too general not too detailed for an intro taste to the topic. The professors were engaging lecturers as well and I found myself quickly going through each week's content to get to the the enjoyable assignments. I'm excited for the other courses in the Specialization.

By Anshul S

Jun 5, 2020

its a very good introduction to machine learning ,although this course is little bit outdated for eg: graphlab is now turicreate and some functions of graphlab doesn't work on turicreate so you have to search the appropriate functions and for handling data they use SFrame which I hate, pandas is much better. but this course is not about what framework they are using its about learning the algorithms that makes machine learning possible and in that case both the instructor did a fabulous job

By Rahul R

Feb 4, 2020

This course is awesome. One of the best course available in Coursera platform. I really appreciate both instructors' hard-work. They are fun loving and enjoy teaching; at the same time they understand, how to make student listen and understand concepts. Both the instructors are really really awesome and genius as they explains every complex concept with simple explanation. Both of them reminds a quote by Albert Einstein -“The definition of genius is taking the complex and making it simple.”

By Cristóbal F d L

Sep 17, 2017

The best online class I ever took. It covers a lot of basic ML algorithms and concepts (with no explanation of details), so you get a nice overview of how this field works and you can move on from there to see what is better for you. I have used the website videos many times to remember what we cover. It also gives you a good exposure to Python. the case study approach is better for understanding the material. I will definitely recommend this class to anyone how wants to know about ML.

By Mayur J

May 28, 2016

I am really liking this course as the instructors are teaching the concept just not theoretically but building a foundation by practical samples and assignement.

The course series is well designed, firstly by this course you get feel of what machine learning is and where all you can apply the concepts. Starting with all the types of ML concepts instructors are building interest among the students.

I would recommend this course to all serious students who want to get into the world of ML.

By Целых Л А

May 8, 2020

It was a really rewarding course! Although I participate with my colleagues and partners in the development of decision-making methods based on a completely different approach, this course was useful for me to reflect on my own scientific position and to increase my competence in achieving my scientific goals. Although, apparently, the main goals for all of us are the same. The teachers were very charismatic! Course - available for understanding and implementation. Thank you so much!

By Jun Q

Jan 13, 2016

I am Jun Qi, a Ph.D. student in the department of Electrical Engineering at University of Washington. I ever took Carlo's excellent machine learning courses at UW and was really feeling pretty good. Although I may become an intermidiate machine learning researcher, I am of great interest in taking his new on-line courses because his new courses are more pracitcal and focus on large-scale data processing. So I highly recommend Prof. Carlos's machine learning courses on the Coursera.


Dec 19, 2015

Two things make me follow this specialization.

Firstly, the Final Capstone mentioned in this course excites me for it is more likely to build a product rather than just to understand some concepts from doing a little programming assignments

Secondly, these techniques are very useful and cool.

But I think this specialization lasts too long and two weeks' material could be done within one week. It is more helpful if other courses in this series would be opened as soon as possible.

By Iñaki D R

Jun 21, 2020

Uno de los mejores cursos para poder entender y practicar en un nivel principiante las principales técnicas de machine learning. Los profesores son excelentes en sus explicaciones y mantienen un gran interés en la clase y sus actividades. Las evaluaciones son objetivas y siempre te brindan el material de apoyo necesario para poder realizarlas. Estoy muy animado de haber cursado este curso y lo considero con la preparación suficiente para tomar los cursos que le siguen.

By Usman I

Oct 24, 2016

For me, this course excelled at brushing up ML concepts I had studied years ago and clarifying the appropriateness of different techniques for different problem settings. However, the best part about this course, and the reason I took it in the first place, was that it introduces participants to a new tool that is scalable for use in larger / production systems.

I am much obliged to the instructors and am sure to continue on to the next course in this specialization.