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

3,600 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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376 - 400 of 566 Reviews for Machine Learning: Classification

By Do A T

Nov 15, 2017

very useful

By Jinhui L

Sep 1, 2016

Good course

By Ayesha N

Jul 19, 2021

good stuff

By Deleted A

May 3, 2020

its useful

By Jan L

Aug 2, 2017

Just great

By 童哲明

Jul 26, 2016

very goog!

By Jair d M F

Apr 21, 2016

Very Good!

By Neha K

Sep 19, 2020



Sep 17, 2020


By Nidal M G

Dec 4, 2018

very good

By 王曾

Nov 27, 2017

very good

By Muhammad H S

Nov 2, 2016


By Joshua C

May 3, 2017


By Roberto E

Mar 1, 2017


By Isura N

Dec 28, 2017


By Anshumaan K P

Nov 11, 2020

NYC ;)

By Shashidhar Y

Apr 2, 2019


By Md. T U B

Sep 2, 2020


By Subhadip P

Aug 4, 2020


By Nicholas S

Oct 7, 2016


By 李真

Mar 5, 2016



Jan 28, 2021


By boulealam c

Dec 15, 2020


By Saurabh A

Sep 11, 2020



Aug 21, 2020