Chevron Left
Back to Machine Learning: Classification

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

4.7
stars
3,671 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

SM

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 :)

SS

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!

Filter by:

1 - 25 of 575 Reviews for Machine Learning: Classification

By Alex H

Feb 7, 2018

By Lewis C L

Jun 13, 2019

By Saqib N S

Oct 16, 2016

By Ian F

Jul 17, 2017

By RAJKUMAR R V

Oct 2, 2019

By Christian J

Jan 25, 2017

By Jason M C

Mar 29, 2016

By Feng G

Jul 12, 2018

By Saransh A

Oct 31, 2016

By Sauvage F

Mar 29, 2016

By uma m r m

Aug 4, 2018

By Dilip K

Dec 21, 2016

By Daisuke H

May 18, 2016

By Ridhwanul H

Oct 16, 2017

By Gerard A

May 18, 2020

By Apurva A

Jun 14, 2016

By Edward F

Jun 25, 2017

By Benoit P

Dec 29, 2016

By Liang-Yao W

Aug 11, 2017

By Paul C

Aug 13, 2016

By Sean S

Mar 9, 2018

By Ferenc F P

Jan 18, 2018

By Samuel d Z

Jul 10, 2017

By Adrian L

Sep 2, 2020

By Yifei L

Mar 27, 2016