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

4.7
stars
3,436 ratings
574 reviews

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 15, 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 16, 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!

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176 - 200 of 542 Reviews for Machine Learning: Classification

By Chandan D

Aug 25, 2018

I really enjoyed learning this course on Machine Learning Classification!

By Zuozhi W

Feb 09, 2017

Very informative class! The lectures are slow, clear, and easy to follow.

By Pankaj K

Sep 25, 2017

Great challenging and deep assignments! Big Thanks to both professors!!

By Zhongkai M

Feb 12, 2019

Great course, provided details that not show in others' and textbooks.

By courage s

Oct 22, 2018

Excellent Teaching with meticulous details and great humor. BIG Plus.

By Tewende J E K

Jul 24, 2016

intuitive, clear and practical. The best explanation I found so far !

By akashkr1498

May 19, 2019

good course but make quize and assignment quize more understandable

By Alexandre N

Dec 20, 2016

Excellent course with plenty of intuition and practical experiments.

By eric g

Mar 21, 2016

The best part for me in this specialization, Classification is great

By Swapnil A

Sep 07, 2020

Really awesome course. Dr. Carlos explains everything from scratch.

By Karthik M

Jun 01, 2019

Excellent course and the instructors cover all the important topics

By Srinivas J

Nov 12, 2016

truly enjoyed this course and recommended to my colleagues as well.

By Thierry Y

Nov 12, 2017

Great material, easy to follow, and nice examples around sushis :)

By Christian R

Sep 11, 2017

The visualizations provide deeper understanding in the algorithms.

By Luis M

Jan 28, 2017

Lots of practical tips, some applicabe not only to Classification.

By Yoshifumi S

May 08, 2016

As always in this specialization, tough course but so practical !!

By japneet s c

Feb 06, 2018

Course is very good. Concepts are explained in a very simple way.

By dragonet

Mar 24, 2016

thank you every much, every helpful! ~i will repeat several time~

By Mark W

May 06, 2017

Fantastic Lecturers and very interesting and informative course

By D D

Oct 16, 2016

Nice videos. Learned a lot. Also videos good for future review.

By Eric N

Oct 12, 2020

Excellent online teaching with clear and concise explanations!

By Parab N S

Oct 13, 2019

Excellent course on Classification by University of Washington

By Mohd A

Aug 14, 2016

Learning is fun when you have professors like Carlos Guestrin.

By Ali A

Sep 05, 2017

the course material is great but the assignments are not good

By clara c

Jun 11, 2016

This course was great! I really enjoyed it and learned a lot.