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

4.7
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

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!

CJ

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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201 - 225 of 443 Reviews for Machine Learning: Classification

By Sandeep J

Sep 04, 2016

Its s great course

By Filipe P L

Oct 03, 2016

Very good, sometimes is a little hard, but is very helpful and have a lot of practical exercises

By Jifu Z

Jul 23, 2016

Good class, But it would be much better if the quiz is open to those who doesn't pay.

By Rashi K

Mar 17, 2016

Assignments were more challenging than previous course. Loved solving them. Enjoyed the optional videos.

By Rishabh J

Dec 19, 2016

Amazing course, Amazing teaching.

By B M K

Oct 16, 2016

Challenging and Exciting Course. Lots of ML concepts (Decision Trees, AdaBoost, Ensembles, Stochastic gradient, loglikelyhood etc. ) are introduced and i believe this course is of extreme importance in laying the fundamentals of ML.

By Weituo H

Mar 14, 2016

Useful and interesting~

By Yifei L

Mar 27, 2016

This is a very good course on classification as previous two.

Good explanation on topics like logistic regression, stochastic gradient descent. The assignments are well designed.

However the decision tree part should introduce entropy and gini which are mainly used for choosing the splitting feature. Also the random forest is worth discussing.

Overall, this is a good course which contains a handful of knowledge.

By Jooho S

Jul 01, 2016

It's very practical.

By Matt Y

Mar 10, 2018

Simply excellent!

By Yuexiu C

Jan 20, 2017

The instructor is awesome. He explained the boring statistical method in a very interesting way!

By Chintamani K

Oct 10, 2017

In detail course for understanding the various concepts of classification. Instead of relying on the libraries, the course focuses on teaching the algorithm implementation using coding language of user's choice. This helps in understanding the algorithms better.

By Mansoor A B

May 02, 2016

I think this is an excellent course to give an idea about the machine learning concept of classification. I felt the lectures were to the point, straight forward and more importantly dealt with practical issues and solutions. The assignments are pretty cool, though large amount of code is written at a few points - I still found them pretty engaging.

By Alexandre N

Dec 20, 2016

Excellent course with plenty of intuition and practical experiments.

By Kim K L

Aug 13, 2016

Another classic and fantastic. Love this Course and learn so much. Highly recommended!

By Sarah W

Sep 24, 2017

Great course! Learned so much! So excited to use this stuff!

By Manuel I C M

May 30, 2017

One of the best courses i've ever tried

By Phan T B

Apr 17, 2016

Very good course

By Zuozhi W

Feb 09, 2017

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

By James M

Jul 20, 2016

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

By 王曾

Nov 27, 2017

very good

By Mohd A

Aug 14, 2016

Learning is fun when you have professors like Carlos Guestrin.

By Chao L

Mar 31, 2017

Nicely formatted. And it's quite intuitive and practical.

By Ornella G

Oct 01, 2016

I really enjoyed the topics presented and the fluid way to present them. It's a very well done summary of the classification models.

By Sudip C

May 03, 2016

Very detailed, Liked optional sections also. Loved it.