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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

ML

Mar 13, 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.Thanks!

KL

Jun 23, 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.

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376 - 400 of 589 Reviews for Machine Learning: Classification

By Manikant R

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Jun 19, 2020

Great course

By Dr. R R N

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May 11, 2020

Nice course.

By Hanna L

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Aug 12, 2019

Great class!

By Illia K

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Oct 25, 2018

Very useful!

By Shuang D

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Jun 28, 2018

nice course!

By Krishna C

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Sep 16, 2017

Great Course

By Krzysztof S

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Jun 6, 2017

great course

By Thuong D H

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Sep 22, 2016

Good course!

By Siddharth V B

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Nov 29, 2020

nice course

By SUBBA R D

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Jun 16, 2020

Nice course

By Aditya P S

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Apr 20, 2020

nice course

By Mr. J

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Mar 22, 2020

spectacular

By Suneel M

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May 9, 2018

Excellent c

By Do A T

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Nov 15, 2017

very useful

By Jinhui L

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Sep 1, 2016

Good course

By Ayesha N

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Jul 19, 2021

good stuff

By Deleted A

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May 3, 2020

its useful

By Jan L

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Aug 2, 2017

Just great

By 童哲明

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Jul 26, 2016

very goog!

By Jair d M F

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Apr 21, 2016

Very Good!

By Neha K

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Sep 19, 2020

EXCELLENT

By PAWAN S

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Sep 17, 2020

EXCELLENT

By Nidal M G

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Dec 4, 2018

very good

By 王曾

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Nov 27, 2017

very good

By Hashim S

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Nov 1, 2016

Excellent