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

4.6
stars
12,275 ratings
2,941 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 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!\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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151 - 175 of 2,853 Reviews for Machine Learning Foundations: A Case Study Approach

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 Mariano C 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.

By 朱顺

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 Muhammad U 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.

By Bayardo E Q T

Jul 30, 2020

First of all, sorry for my English. The course is a very interesting first experience with the machine learning and give a base by

all the panorama, the class are funny and easy to understand, and the professors are excellent with the explanation. I really recommend this course for all the people that be start with this new world that is the ML.

Pd. by the way, for the people that no speak a very well English, don't worry, is easily to understand both professors.

By Leonardo M d O

Aug 25, 2018

Amazing course. I had already done other ML Courses at coursera, but the competitive differential is the friendly approach took by the professors. Carlos and the other girl are very nice, they smile...so the training gets less formal, they look like a friend telling stories in a bar. Another main point is really the uses cases. They swap between the big forest map and the detailed view of the leaf in a succinct way. Easy to understand both views. Congratulations.

By Himadri M

Dec 17, 2015

The Course literally boosts off one's confidence in ML, and gives one the confidence to proceed to higher levels in ML.

Great Course, Superb Instructors and Excellent Course material ! The complete ingredient for a perfect starter course !

I would like to mention that i came to know how to write a review using the words: Great , Good , Superb and Excellent , so that my review is rated above others by the algorithm !

So thanks Carlos sir for this superb course ! :D

By Gurmeet S G

Jul 10, 2020

A good start to ML. Light on the programming / algorithms but that helps focus on the concepts which is appropriate for a complete beginner to ML.

It took a while to get set up because of the Python and turicreate versions for Mac OSX so I almost dropped out of this course. Glad I kept to it though. Maybe more value in using the standard packages to make skills comparable to others, but I appreciate that turicreate has been optimised for large datasets.

By Abhijit D

Nov 25, 2017

Excellent course presentation by Emily and Carlos - If courses are presented in this interactive manner learning will always be fun and interesting.

Always advisable to have some basics on python , data frame , machine learning(if possible) and you will go really smooth with this intermediate level course.

Course material really good for machine learning with real case studies and capstone project on deep learning was indeed the crown of the course.

By Louis U

Nov 15, 2017

Absolutely awesome! I am really appreciative of the time and efforts on the part of the instructors and the University of Washington to make Machine Learning very accessible. The concepts were very easy to grasp and I endorse the case study approach as a effective introduction to complex topics. Obviously, it will get more detailed and complex in upcoming courses in the specializations but I feel very prepared and excited to learn. Thank you.

By Syed M Z H K

Aug 5, 2017

Thanks alot for this awesome course. As because of it, I was able to learn python (otherwise I used to hate it, when I started learning it with OpenCV) and ipyhon (which is an awesome tool). Furthermore, thanks alot course era for providing me with this amazing fee waiver (since I can't afford this course) , as because of this I am hoping to excel in this field after completing this specialization, in order to later land good job. Thank you!

By Prachur B

Dec 27, 2016

A very practical approach for learning and get excited about Machine Learning. The python notebook exercises really help if you do them diligently (though sometimes it was too easy because of hints, may be hide them and who when someone asks for it). The mention of so many concepts and algorithms can be overwhelming, so a clear guideline on how to leverage the material specifically in this foundation course in the closing remarks would help.

By AMAN M

Dec 18, 2018

I was totally new to the machine learning, but this course helped me to understand what is it? What is the importance of it ? where it can be used and what will be the future of it ? There was also enough exercise work to check our understanding to the topic learnt. I think it will be more interesting if they provide a console for code snippet for the assignment... It was very nice experience with Carlos Guestrin Sir and Emily Fox Ma'am

By Tobi L

Dec 6, 2015

I appreciate that the first course focused on applications, I've got plenty of math and programming experience, but I took this specialization to really grok machine learning and its applications. By using graphlab as a black box and focusing on specific applications, I really understood why these techniques are useful. Once I've got the why, I feel much more motivated to dig deeper into the how, which I feel confident enough that I can do.

By Aleksander S

Feb 1, 2019

This is a great course. The content is delivered at a very good pace even for people with little prior knowledge of statistics or computer science — not too fast (would be too difficult) and not too slow (could become boring). Additionally, the assignment model is perfect — it requires completing hands-on exercises, but then the solution is assessed using simple quizzes. Thanks to that the answers and the grades are immediately available.

By George C

Dec 26, 2015

The case study approach and the reliance on GraphLab library makes it easier to get your head around the concepts before going into the detail later in the specialization. I learn better when I have a working understanding of the high-level concepts and the use for a new area of study. This course provides that high-level understanding and the later specializations provides the deep dive. Also, the course seemed well paced and structured.

By Chengcheng L

Dec 27, 2015

This is a wonderful course to get you into the door of machine learning. It covers several key concepts in ML. The videos are easy to follow. The assignments are not difficult to complete if you do the "follow along" exercises. You won't be able to understand the theoretical background of the algorithm very well after taking this course, but you can apply Grahphlab functions to whatever data you have and generate quick and dirty results.

By Evan S

Mar 11, 2019

This course was a great balance between lecture (and lecture quiz) & iPython lecture (and iPython lecture quiz). I like that the answers are multiple choice as opposed to copying and pasting code. That way, any coding errors can be played around with in the notebook first without using up any submission attempts. Emily and Carlos did a great job of keeping the course fun while sticking to the easy-to-understand case-study approach.