Chevron Left
Back to Machine Learning: Classification

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

4.7
stars
3,736 ratings

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

DM

Apr 30, 2020

Good Class. Program assignment have a bit too much hand holding, which made them easier and less useful than they might have been if they were allowed to be more challenging.

KL

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.

Filter by:

276 - 300 of 589 Reviews for Machine Learning: Classification

By Komal Y

•

Nov 12, 2024

good for knowledge, i learned alot

By Cosmos D I

•

Mar 29, 2020

This course is very informational!

By Alessandro B

•

Oct 31, 2017

nice, clear engaging ...and useful

By 易灿

•

Nov 28, 2016

课程很生动，讲的很详细，真心谢谢导师！希望能在算法后面多提供点资料！

By Henry H

•

Nov 18, 2016

Very clear and easy to understand.

By Angel S

•

Mar 8, 2016

Awesome. Waiting for the next one.

By Jing

•

Aug 14, 2017

Better than the regression course

By Rishabh J

•

Dec 20, 2016

Amazing course, Amazing teaching.

By CHERUKURI S V N K

•

May 29, 2020

IT WAS EXCELLENT AND ENJOYED IT.

By Fernando B

•

Feb 22, 2017

Best Course on ML yet on the Web

By Pranas B

•

Jul 2, 2016

Good practice and bit of theory.

By Andrew M

•

Jun 15, 2016

I came here to learn. I learned.

By zhenyue z

•

Jun 3, 2016

good lecture, good for everyone.

By Pakomius Y N

•

Sep 29, 2020

Sangat Bermanfaat,Terima Kasih.

By Manuel T F

•

Jul 21, 2017

Really great course. Well done!

By tonghong c

•

Jun 15, 2017

Best ML course I've ever taken!

By Sandeep K S

•

May 7, 2016

awesome course awesome teachers

By Vijai K S

•

Mar 5, 2016

Heck yeah!! its finally here :D

By Định H

•

Apr 24, 2024

Number of assignment is huge !

By Vinothkumar G

•

Jul 11, 2020

Very useful learning platform.

By Jinho L

•

Jul 20, 2016

Very pragmatic and interesting

By Snehotosh B

•

Mar 21, 2016

Excellent and very intuitive.

By Neemesh J

•

Oct 28, 2019

Awesome learning experience.

By Fan J

•

Aug 4, 2019

good content, help me a lot!