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

2,875 ratings
479 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


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!


Jan 25, 2017

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses

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76 - 100 of 447 Reviews for Machine Learning: Classification

By Mike M

Jul 16, 2016

Learned a lot, great course!

By Daopeng S

Apr 12, 2016

A very good introduce machine learning course, it's clear and easy to follow.

By Lars N

Oct 04, 2016

Best course taken so far!

By Albert V d M

Mar 08, 2016

Very instructive, you learn a lot.

By Marios A

Mar 08, 2016

The course is really well structured and gives a solid understanding in the latest approaches in Machine Learning. However I would also like to see in this course more sophisticated math, because it matters and I think there are important.

By Akshay B

May 24, 2017

Excellent and intuitive introduction to classification.Certainly a lighthouse in a rather overwhelming and chaotic learning scenario of machine learning we have now a days(Highly recommended for both mathematics and programming student)

By 易灿

Nov 28, 2016


By James M

Jul 20, 2016

Top notch. Great course design. Best value for money in Machine Learning!

By Nitish V

Jul 06, 2017

The course is well designed for both beginners and experts . The concepts are well explained and the assignments are really challenging. Best thing is , it talks more from practical aspects . The optional sections are really good.

By Ankit S

Jun 08, 2016

Really nice course!

By M L

Mar 14, 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.


By Angel S

Mar 08, 2016

Awesome. Waiting for the next one.

By Zizhen W

Nov 03, 2016

Pretty Solid!

By Shiva R

Apr 16, 2017

Exceptional and Intutive

By Dauren B

Jan 27, 2018

I loved this course! It is designed in a way that both beginner and more advanced student can grasp knowledge. New things for me like boosting (ensemble models), decision trees, stochastic gradient descent, online learning (which is not used much by big systems, instead they tend to do something different for incoming new data) and much more are introduced and explained in this course. Recommend 10/10.

By Pranas B

Jul 01, 2016

Good practice and bit of theory.

By Snehotosh K B

Mar 20, 2016

Excellent and very intuitive.

By Tony T

Nov 19, 2016

funny and enthusiastic lecturer make a dry subject more fun.


Aug 03, 2016

A great combination between down to earth concepts and their implementations in python. Implementation of topics in plain python is what I enjoyed the most.

By kazi n h

Jun 23, 2016

One of the awesome course on classification. Just so perfect for learning.

By Kaixiang Y

Jun 27, 2017

Very good instructors

By 李今晖

Sep 01, 2016

Good course

By Abhishek T G

Jun 22, 2016

The quizzes can be a bit more challenging

By Sandeep J

Sep 04, 2016

Its s great course

By Jooho S

Jul 01, 2016

It's very practical.