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

3,612 ratings
597 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

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 :)

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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326 - 350 of 566 Reviews for Machine Learning: Classification

By Kurt K

Apr 16, 2016

Excellent course !


Jun 6, 2020


By Aparna g

Jan 30, 2020

very Good Concept

By Germanno T

Dec 4, 2019

Excellent Course!

By Miguel Á B P

May 21, 2019

Excellent course!

By parv j

Mar 3, 2019

Brilliant course!

By Mayank C

Apr 12, 2018

Loved this course

By Matt Y

Mar 10, 2018

Simply excellent!

By Jonathan H

Jun 16, 2017

Excellent course!

By Le D L

May 2, 2017

Lots of knowledge

By Prabal T

Oct 5, 2016

Excellent course!

By André F d A F C

Jul 25, 2016

Excellent course.

By V S

Apr 28, 2016

Best course ever!

By Huynh L D

Mar 10, 2016


By VijayaLakshmi A

Aug 10, 2021

Good explanation

By Sukhvir S

Jul 10, 2020

Great Experience

By Phan T B

Apr 17, 2016

Very good course

By Michelle B

Jul 15, 2021

Well explained.


Aug 1, 2020

best course....

By Jesús U S

Jun 26, 2020

Awesome Course!

By Jerome Z

Jul 4, 2018

Very good class

By Paulo R M B

Jan 30, 2017

Well explaned !

By Pandu R

Apr 20, 2016

Worth the wait.

By Roberto C

May 18, 2020

Simply amazing


May 4, 2020

Very well done