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

2,902 ratings
482 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


Oct 16, 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!


Jan 25, 2017

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses

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201 - 225 of 450 Reviews for Machine Learning: Classification

By Rishabh J

Dec 19, 2016

Amazing course, Amazing teaching.

By Hristo V

Dec 01, 2016

The course is absolutely amazing! Very clear explanation of the concepts with great notebook assignments.

By 易灿

Nov 28, 2016


By Akshay B

May 24, 2017

Excellent and intuitive introduction to classification.Certainly a lighthouse in a rather overwhelming and chaotic learning scenario of machine learning we have now a days(Highly recommended for both mathematics and programming student)

By Isura N

Dec 28, 2017


By James M

Jul 20, 2016

Top notch. Great course design. Best value for money in Machine Learning!

By Ali A

Sep 05, 2017

the course material is great but the assignments are not good

By 王曾

Nov 27, 2017

very good

By Zuozhi W

Feb 09, 2017

Very informative class! The lectures are slow, clear, and easy to follow.

By M L

Mar 14, 2016

Great course!

Personally I could use a little more on the math behind the algorithms (e.g. Adaboost, why does it work?).

Also, would be great to add SVM in next iterations of this class.


By Angel S

Mar 08, 2016

Awesome. Waiting for the next one.

By Zizhen W

Nov 03, 2016

Pretty Solid!

By Abhishek T G

Jun 22, 2016

The quizzes can be a bit more challenging

By clark.bourne

May 09, 2016

Professional, comprehensive, worth to learn

By Carlos L

Jun 10, 2016

The contents are really clear and professors are great!

By Itrat R

Jan 23, 2017

Excellent Course!!!

By zhenyue z

Jun 03, 2016

good lecture, good for everyone.

By Krisda L

Jun 24, 2017

Great course. I learned a lot about Classification theories as well as practical issues. The assignments are very informative providing complimentary understanding to the lectures.

By Saheed S

Jul 18, 2017

It was a great course, I will start working on a new classification project. Thanks

By Fabio P

Apr 18, 2016

Very interesting topic with some advanced topics covered. It really shows how to use machine learning in the real world.

By Dhruvil S

Jan 10, 2018

Nice Course Clears a lot of concepts.

By Daisuke H

May 18, 2016

I really love this Classification course as well as Regression course!! This course is covering both mathematical background and practical implementation very well. Assignments are moderately challenging and it was a very good exercise for me to have a good intuition about classification algorithms. I only used standard Python libraries such as numpy, scikit-learn, matplotlib and pandas, and there were no problems for me to complete all of the assignments without any use of IPython, SFrames, GraphLab Create at all. I would say thank you so much to Carlos and Emily to give me such a great course!!

P.S. This course would be perfect if it covered bootstrap and Random Forest in details.

By Anurag U

Jan 16, 2017

Best source to learn classification techniques

By 李紹弘

Aug 14, 2017

This course provides me the very clear concept.

By Bert B

Oct 20, 2016

Very well done course.

Would be nice to have many more very short examples during the lectures that match the formulas. This would help me understand the formulas much better since I do not have a calculus or linear algebra background.