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

4.7
stars
3,541 ratings
588 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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426 - 450 of 556 Reviews for Machine Learning: Classification

By Jaime A C B

Sep 12, 2016

Sometimes is difficult to understand the concept behind Classification because some videos are more practical than theorical, I mean it could be better to start the video explaining some concepts and then show and explan some samples and theorical issues.

Thanks.

By Nicolas S

Jan 2, 2020

The course itself is well structured and introduce gradually the complexity. Unfortunately, the exercises requires the use of a specific library, instead of scikit-learn and numpy. Furthermore, they also required Python 2, while Python 3 is now widely used.

By Martin B

Apr 11, 2019

As with all the courses in this specialization: great production values, excellent tuition. Useful assignments, even though the reliance of Graphlab Create is a bit of a drag. I also would have liked to see some discussion of Support Vector Machines.

By J N B P

Oct 9, 2020

This course covers all the core algorithms used in Classification models. If you have a basic understanding of machine learning, this course can help you build your understanding of classification on a deeper level.

By Uichong D L

Sep 17, 2017

Using discontinued Graphlab in the programming assignment is a minus and low activities in the forum makes hard to find assistance from the communities or mentors but the course material itself is just great.

By João S

Apr 18, 2016

Very good content, very well explained... great course. Classification its a very broad topic but i think this is great introduction.

The hands on where kinda on the easy side... but very interesting.

By David F

Aug 7, 2016

Not as good as the previous courses in this specialization - I agree with those who have noted that this one seemed a little rushed. However, these are still the best courses I've found on Coursera.

By Ahmed N

Feb 22, 2018

Great knowledge about machine learning fundamentals, More math illustration needed though it's great knowledge and very great basics about different machine learning algorithm used in reality

By Eric M

Apr 15, 2017

Extremely clear and informative. Good introduction to ML. I felt the labs could have had us write a little more of our own code, and would have been better to use non-proprietary libraries.

By Dawid L

Mar 20, 2017

Presented content is rather clear and instructors are rather easy to follow. Only the assignments are often confusing as there are questions which refer to missing content.

By Thuc D X

Jun 27, 2019

Sometimes the assignment description was hard to follow along. Overall, the course equips me a good understand and practical skills to tackle classification tasks.

By Gaurav K J

May 1, 2018

I learnt a lot, but I feel course 2 was very well made and this one felt a bit unstructured in comparison. Also, assignments in this course were made very easy.

By Justin K

Jun 10, 2016

Assignments were a little too easy, considering that students are expected to have taken the first two courses in the specialization. Otherwise, great course!

By Hao H

Jun 12, 2016

Good course overall. Some difficult materials such as boosting were not clear enough and I had to look into a few online resources to really understand it.

By 김대성

Mar 23, 2021

Very nice lecture & materials. The only slight negative component this lecture contains is the library used for the programming assignment.

By Fangzhe G

Feb 7, 2020

This course could be better if more programming content was taught. The programming assignments are difficult and not taught in courses.

By Brian B

Apr 22, 2016

Great course. I'm really looking forward to learn more about clustering in the next course since I know nearly nothing about clustering.

By Fahad S

Nov 3, 2018

The content was excellent and the exercises were really good. It would be better if svms and bayesian classifiers are also covered

By Aaron

Jul 3, 2020

Nice course for new learner of machine learning, but I do hope this course could have introduction to support vector machine.

By Alexis C

Sep 29, 2016

wanted more sophisticated mathematics and intuition (as opposed to simpler explanations). [regression course had this ...]

By Kishaan J

Jul 1, 2017

Really loved this course! The insights into decision trees and precision-recall couldn't have been any better! Thank you!

By Raisa

Aug 19, 2017

Wanted some stuff on SVM and Dimensionality Reduction. Awaiting for a course on Recommender Systems and Deep Learning

By Ning A

Sep 16, 2016

Learn more than just classification, but also learn how to understand the ideas behind classification algorithms.

By Yingnan X

Apr 14, 2016

A good course to start learning classifications and getting exposure to algorithms. The instructor is awesome!!

By Oleg R

Oct 9, 2016

I would prefer more complex assignments and more advanced math concepts in the course. Otherwise it is great.