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

2,844 ratings
475 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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26 - 50 of 443 Reviews for Machine Learning: Classification

By Tripat S

Jun 24, 2016

This is the best course ever that can happen in ML...I did not know anything, but after taking this specialization, my understanding of ML has dramatically improved

Would recommend without any reservation - Prof Gustrin and Prof Fox are the best!!!

By Evgeni S

Jun 11, 2016

Very focused overview of different classification methods. Goes deeper than in other ML classes.

By Joshua C

May 03, 2017


By Shaowei P

Mar 31, 2016

great course, would have been even more great if there are more details on how to use boosting for kaggle

By Krishna C

Sep 16, 2017

Great Course

By Sanjay M

Jun 30, 2017

Very nice course with good mix of machine learning concepts with maths, programming.

By Jenny H

Jan 01, 2017

All courses in this series are organized and taught in an extremely efficient manner. I have learned so much out of them and they have helped me with my current job and my next job search!

By Binil K

Jul 30, 2016

Nice Course, very much helpful and reccomended

By Pandu R

Apr 20, 2016

Worth the wait.

By Richard L

Oct 15, 2016

Great course. The lectures and programming assignments have been extremely beneficial to help me get a basic foundation of ML classification.

By Simon C

Oct 28, 2016

Great content and exercises which facilitated understanding of very complex concepts.

By Willismar M C

Nov 19, 2016

Amazing Course Module, I learned a lot of concepts for classifications using Decision Trees, Logistic Functions, Boosting, Ensemble and way to attack problems. Also a lot of coding with Graphlab, I personally like to program by my own but I also appreciating the tool for the class and comparing my skills with other tools. Very cool ! Nice Class

By Yoshifumi S

May 08, 2016

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

By Sundar J D

Apr 23, 2016

Overall a great course and has a very good instructor. Teaches you all the fundamentals behind classification algorithms and models. Contains very good assignments/projects that make you implement the models yourself to get a much better understanding of the concepts.

By m w

Dec 24, 2017

While I enjoyed most of the exercises, I found some of the implementations to be more puzzle solving rather than deeply understanding the algorithms.

By Alex L

Mar 08, 2016

Great courses as usual like the previous courses in this specialization. Cater for beginners who want to gain a strong foundation and practical usages for ML.

By Renato R S

Aug 27, 2016

All the basics - and much of the advanced stuff - is presented, in a coherent and inspired way. Thanks for crafting such a course.

By Ashish

Oct 26, 2016

I appreciate the way Emily and Carlos explain the concepts. Its very intuitive for beginners and optional sections give further details. The datasets used in programming assignments are taken from real world examples.

Overall an excellent course and really looking forward to completing the series.

Kudos to Carlos, Emily and the team.

By Syed A u R

Aug 11, 2016

exceptional course. Carlos did an excellenet job

By David E

Aug 21, 2016

very useful course : covers a range of very practical and useful topics I had heard about but didn't fully understand until taking this course. Some highlights stochastic gradient, boosting, and precision-recall trade offs.

By V S

Apr 28, 2016

Best course ever!

By Anwarvic

Dec 05, 2016

This course is awesome, specially the assignments. In this course, I've implemented most of the famous ML algorithms that our world is now using.

I can't describe how happy I am. Before this course, I looked at machine learning as a difficult field which can't be understood no matter what. Today, I'm capable of doing some great effort.

Thank You so much :)

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.

By Mayank C

Apr 12, 2018

Loved this course