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Learner Reviews & Feedback for Machine Learning: Classification by University of Washington

4.7
stars
3,470 ratings
577 reviews

About the Course

Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended)....

Top reviews

SM
Jun 14, 2020

A very deep and comprehensive course for learning some of the core fundamentals of Machine Learning. Can get a bit frustrating at times because of numerous assignments :P but a fun thing overall :)

SS
Oct 15, 2016

Hats off to the team who put the course together! Prof Guestrin is a great teacher. The course gave me in-depth knowledge regarding classification and the math and intuition behind it. It was fun!

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476 - 500 of 545 Reviews for Machine Learning: Classification

By Ragunandan R M

Sep 17, 2018

Good overall course.

By Appu K

Nov 11, 2020

content is complete

By Lim W A

Nov 21, 2016

Learnt new things.

By Mehul P

Aug 17, 2017

Nice explanation.

By gaozhipeng

Jun 30, 2016

good introduction

By Alberto B

Mar 17, 2018

Very good course

By Antonio P L

Apr 30, 2016

Fantastic Course

By Anand B

Aug 7, 2017

Great course!

By Ayswarya S

Feb 5, 2019

best course

By Alberto J L R

Oct 12, 2017

Good Mooc

By Syamsul B

Aug 31, 2020

Great

By VIGNESHKUMAR R

Aug 23, 2019

good

By serge b

Jul 2, 2016

good

By IDOWU H A

May 20, 2018

B

By Ole H S

Jun 16, 2016

First. I like these courses allot. They are pretty close to covering just what you need to actually do machine learning in the real world and not dive too deep into topics that have no practical value.

However:

This course was a bit too thin, the last 4 weeks of the course contained little in depth informations and seemed to brush over allot of different topics that could have contained more information. Although they where important topics the course could go more in depth on at least 3 or 4 of those topics. The last 3 weeks could have been a course on its own if properly explored. However the concepts are well enough covered to be usable in practice i belive.

The programming exercises where ridiculously simple. Everything was reduced to filling in 1 or two lines in a bigger function. I understand that the point was to see how these functions are made and that it increases our understanding of the algorithms already existing in packages like schikit-learn and graphlab. Also the content became a bit too repetetive (actually started in the second course but continues in this course). The time used on variation over the same topic in different models made it challenging to pay attention when the lecture finally came to a new point (brain fell a sleep while waiting for something new).

By Ryan M

Aug 25, 2020

While I feel like I have a good theoretical understanding of the issues involved in classification, with an understanding of how the algorithms work and how to implement them, this course could have prepared me better to attack an actual problem by following a real case study through, showing me what steps someone with experience in attacking real problems would take in order to come up with a good classifier.

In particular, while a number of classifiers were presented, there was little to no discussion of the relative advantages and disadvantages of each algorithm. In what cases should I choose logistic regression? A decision tree or a boosted decision tree?

Finally, it seems that random forests and support vector machines are common classifiers, and this course did not cover them. I instead had to learn about random forests (a relatively simple concept that could have been included with the boosted decision tree content) from scikit-learn's web site.

By Ziyue Z

Aug 10, 2016

Compared with the regression course, this course was a slight disappointment. 1. there is less material compared to the regression course. Maybe this is because classification concepts are more intuitive. 2. the slides are much less prepared. Some of the sides even re-use earlier lesson slides in the beginning as a "review", much like soap operas re-use scenes from earlier episodes as "memory recall" to fill air time. 3. the math is more handwavy compared to the regression course. Neither course are supposed to go in depth with proofs, but I felt the regression course was at the right level and this course degraded too far. Do note it's very possible that I'm biased because I have seen more of the material from this course than the regression course.

By Sunil N

May 2, 2020

Bit of skewed distribution of load of work. Like week 6 and 7 were extremely light (merely 1 hour work), while week 2 and 5 were too heavy for a week. Syntax errors in assignment notebooks kept the nerves active but can be bit frustrating for relatively naive or trusting candidates, who might end up spending a lot of time finding bugs in their own piece of code. Overall a nice experience. Covid and wfh situation is not allowing proper time for learning but reminders helped in meeting the goal. Thank you

By 오승윤

Dec 3, 2016

Turi stopped working on SFrame (at least on Github), and SFrame does not supports Python 3. Expect some difficulty if you use other tools like pandas - the programming assignment completely assumes you use SFrame. Fortunately data of csv format is provided, so you can complete it anyway but again, don't expect a smooth ride.

Also the lecture tends to cover general concepts than mathematical details. I don't like it, but that would be a good point to the starters.

By Tom L

Oct 21, 2016

Well, after the regression course, which I actually found interesting, the classification course doesn't look so good. The programming assignments are mostly pointless. The use of graphlab doesn't make it better. The info presented in this course is rather superficial. If you're entirely new to machine learning, you could find some value in this course. If not, go buy a good book.

By Oliverio J S J

Jun 17, 2018

At first the course seems interesting but, as it progresses, it fails to convey why these contents are important in the deep learning era. In addition, it seems quite obvious that some contents are missing; I suppose that they have been eliminated due to the same problems that forced the cancellation of the last specialization courses.

By Francesco

Nov 15, 2019

The material is good, but the choice of using GraphLab Create is a poor one. It's not used in the industry and it's poorly supported. I had issues installing it both via command line and via the installer, so I ended up using the AWS machine. But that has it's own drawbacks, such as the slowness and the setup time.

By Nitzan O

Apr 25, 2016

The course is interesting and well taught. The professor is very enthusiastic and it makes the course fun to watch. The problem in my opinion is that the content is too superficial. It's completely lack of mathematical background and the programming exercises are sometimes no more than copy paste.

By ANIMESH M

Sep 4, 2020

The course is up to the mark but what i felt missing is about the coding . They didn't focus on implementation tasks simply gave the notebooks for the assignments.

Also S.V.M and random forest classifiers are missing.

From my side concluding all the experience , i will give a 6.5 out of 10.

By Kumar B

Oct 4, 2017

This course covers the basics of classification very well, but I would have liked optional sections on more advanced topics. Some of the quiz questions were a bit confusing. It would have been good if the exercises also dealt with unbalanced data sets in more detail.