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

2,875 ratings
479 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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101 - 125 of 447 Reviews for Machine Learning: Classification

By Matt Y

Mar 10, 2018

Simply excellent!

By Yuexiu C

Jan 20, 2017

The instructor is awesome. He explained the boring statistical method in a very interesting way!

By Chintamani K

Oct 10, 2017

In detail course for understanding the various concepts of classification. Instead of relying on the libraries, the course focuses on teaching the algorithm implementation using coding language of user's choice. This helps in understanding the algorithms better.

By Mansoor A B

May 02, 2016

I think this is an excellent course to give an idea about the machine learning concept of classification. I felt the lectures were to the point, straight forward and more importantly dealt with practical issues and solutions. The assignments are pretty cool, though large amount of code is written at a few points - I still found them pretty engaging.

By Weituo H

Mar 14, 2016

Useful and interesting~

By Rashi K

Mar 17, 2016

Assignments were more challenging than previous course. Loved solving them. Enjoyed the optional videos.

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 Alexandre N

Dec 20, 2016

Excellent course with plenty of intuition and practical experiments.

By Phan T B

Apr 17, 2016

Very good course

By Kim K L

Aug 13, 2016

Another classic and fantastic. Love this Course and learn so much. Highly recommended!

By Sarah W

Sep 24, 2017

Great course! Learned so much! So excited to use this stuff!

By Zuozhi W

Feb 09, 2017

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

By Isura N

Dec 28, 2017


By Marcio R

Jun 14, 2016

Curso excelente, desde o material, as atividades práticas e aulas. O fórum de discussões é repleto de pessoas interessadas em ajudar. Essa é a especialização a longa distância definitiva de Machine Learning.


Jun 14, 2017

Best ML course I've ever taken!

By Rahul G

May 06, 2017

Excellent course except that week 7 th assignment based on ipynb notebook had some redundant questions. Otherwise a good course especially sheds light on Adaboost, ensemble classifiers and stochastic gradient with batch processing.

Thanks Professor Carlos.

By Etienne V

Nov 13, 2016

Great course with very good material! I'd like to see assignments that leaves more coding tasks to the student.

By Farrukh N A

Feb 10, 2017

I found carols to be the best instructor in machine learning domain, he presented the algorithms and all core machine learning concepts in really great way.

By Paulo R M B

Jan 31, 2017

Well explaned !

By 王曾

Nov 27, 2017

very good

By Mohd A

Aug 14, 2016

Learning is fun when you have professors like Carlos Guestrin.

By Chao L

Mar 31, 2017

Nicely formatted. And it's quite intuitive and practical.

By Daniel Z

Mar 08, 2016

This is a hand-on very exciting course, strongly recommended for all audience

By Sauvage F

Mar 29, 2016

Excellent Course, I'm very found of Carlos jokes mixed with the hard concepts ^^. Lectures are precise, concise and comprehensive. I really enjoyed diving in depths of the algorithms' mechanics (like Emily did in the Regression Course). I also deeply appreciated the real-world examples in the lectures and real world datasets of assignments.

Some may regret the absence of a few "classic" algorithms like SVM but Carlos definitely made his point about it in the forum and did not exclude the addition of an optional module about it.

I found some of the assignments less challenging than during the Regression Course, but maybe I'm just getting better at Machine-Learning and Python ^^.

Thanks again to Emily and Carlos for the brilliant work at this very promising specialization.