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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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326 - 350 of 589 Reviews for Machine Learning: Classification

By Michael P

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Dec 6, 2016

Awesome, not awful;)

By 쥬

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Jun 30, 2016

It's very practical.

By Vatsal M

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Oct 16, 2024

very helpful course

By AJAY K

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Oct 13, 2019

Excellent tutorials

By Muhammad Z H

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Aug 30, 2019

I have learned alot

By Luis E T N

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Jul 4, 2017

Excelent! Congrats!

By Itrat R

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Jan 22, 2017

Excellent Course!!!

By Roger S

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Sep 4, 2016

This course is COOL

By Ankit S

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Jun 8, 2016

Really nice course!

By N P

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Jul 20, 2020

wonderful lectures

By Mrs. G A D

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May 13, 2020

Wonderful learning

By Sandeep J

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Sep 4, 2016

Its s great course

By Kurt K

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Apr 16, 2016

Excellent course !

By DEEPAK P

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Jun 6, 2020

ULTIMATE TEACHING

By Aparna g

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Jan 30, 2020

very Good Concept

By Germanno T

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Dec 4, 2019

Excellent Course!

By Miguel Á B P

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May 21, 2019

Excellent course!

By parv j

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Mar 3, 2019

Brilliant course!

By Deleted A

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Apr 12, 2018

Loved this course

By Matt Y

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Mar 10, 2018

Simply excellent!

By Jonathan H

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Jun 16, 2017

Excellent course!

By Le D L

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May 2, 2017

Lots of knowledge

By Prabal T

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Oct 5, 2016

Excellent course!

By André F d A F C

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Jul 25, 2016

Excellent course.

By V S

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Apr 28, 2016

Best course ever!