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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

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.

IF

Jul 17, 2017

Good overview of classification. The python was easier in this section than previous sections (although maybe I'm just better at it by this point.) The topics were still as informative though!

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351 - 375 of 589 Reviews for Machine Learning: Classification

By HARSH R

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Nov 4, 2024

excellent course

By VijayaLakshmi A

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Aug 10, 2021

Good explanation

By Sukhvir S

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Jul 10, 2020

Great Experience

By Phan T B

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Apr 17, 2016

Very good course

By Mich

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Jul 15, 2021

Well explained.

By PUNEET K G

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Aug 1, 2020

best course....

By Jesús U S

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Jun 26, 2020

Awesome Course!

By Jerome Z

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Jul 4, 2018

Very good class

By Paulo R M B

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Jan 30, 2017

Well explaned !

By Pandu R

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Apr 20, 2016

Worth the wait.

By Roberto C

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May 18, 2020

Simply amazing

By HOUESSOU R T

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May 4, 2020

Very well done

By Manan M

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Apr 20, 2020

Amazing course

By avishek k

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Jul 19, 2020

great content

By Gaurav G

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Dec 26, 2018

Good Course!!

By sankar b

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Oct 30, 2018

Good learning

By Yang X

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Oct 29, 2017

Very helpful!

By Omar B

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Feb 9, 2017

Great course.

By Zizhen W

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Nov 3, 2016

Pretty Solid!

By Manuel S

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Sep 11, 2016

Great course!

By Salim T T

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Apr 13, 2021

Good course!

By Francisco R M

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Feb 21, 2021

Great course

By Sumit K J

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Jan 24, 2021

Great Course

By Aaqib M

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Sep 20, 2020

Great course