Chevron Left
Back to Machine Learning: Classification

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

4.7
stars
2,971 ratings
490 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

Filter by:

76 - 100 of 458 Reviews for Machine Learning: Classification

By Shazia B

Mar 25, 2019

one of the best experience about this course i gained I learned a lot about machine learning classification further machine learning regression thanks a lot Coursera :)

By Fakrudeen A A

Sep 15, 2018

Excellent course - teaches linear, logistic regression and decision trees. It also teaches the most important concept of precision-recall. Overall highly recommended.

By Marcus V M d S

Oct 16, 2017

Another great course from this specialization. Tremendous effort in making the notebooks and assignments. I just think there could be recommended readings also.

By ZHE C

Mar 26, 2017

effective teaching and practice about decision tree, boosting, and logistic regression. Could have a little more practice on gradient boosted tree/random forest

By Niyas M

Oct 29, 2016

Amazing course! Packed with insights, reasoning and Carlos's humor and wit. Highly recommended for novices (along with the Machine Learning Foundations course).

By Leon A

Mar 10, 2016

Course material selection, pace and presentation are all well thought out. This sequence of courses in the Machine Learning specialization is truly exceptional.

By lokeshkunuku

Jun 12, 2019

its been 3 weeks I started this course it was so nice and awesome. the lectures explaination and the ppt all were well crafted and easy to pick and understand.

By Bharat J

Jan 19, 2018

I wish we had 5th course too,All courses are well organized and can be completed with other tool.

Hope they also include SVM and start courses on deep learning

By Ganesan P

Feb 06, 2017

A very good course - understood a lot about classification and the understanding gained will help in reading text books like Ian Good Fellow for deep learning

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 Babak P

Jun 28, 2018

Great exposure that requires hand coding the algorithms. Really makes the concepts stick with a perfect combination of theory and programming mixed together.

By Farrukh N A

Feb 10, 2017

I found carols to be the best instructor in machine learning domain, he presented the algorithms and all core machine learning concepts in really great way.

By OG

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 Jane z

Jan 26, 2020

The hands-on approach is excellent. Not only I learned ML / Classification, I was able to practice Python skills and statistical skills as well.

THANK YOU!

By Nikolay C

Mar 16, 2016

Excellent course! I've learned these topics before, but many things were not clear enough. While learning this course my knowledge really improved a lot.

By Usman

Nov 13, 2016

I think support vector machines is an important topic which is missing. Anyway, the programming assignments were terrific. I really enjoyed this course!

By Andrea C

Sep 07, 2016

The course covers most important topics in depth and exercises are very interesting, them helps you to reason about some important theoretical concepts.

By Youssef R

Aug 23, 2017

This is really a wonderfull course, and i recommend it to anyone who want to master some important techniques in the trending field of machine learning

By Josef H

Nov 27, 2016

I like the detailed comparison between choosing different parameters for creating the classification model. I learn a lot of tricks for creating plots.

By Suoyuan S

Apr 21, 2016

This course is friendly to machine learning beginners for the learning material is easy to understand as well as the assignment is easy to accomplish.

By Sara E E

Mar 29, 2018

It is very intuitive and easy to follow.

I hope you add SVM and talk about linear/nonlinear decision boundaries in the next enhancement to the course.

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 Gunjari B

May 21, 2018

An absolute marvel of a course! In depth explanation to everything, detailed and important concepts explained so much at ease with Carlos' humour!

By RAMESH K M

Aug 01, 2016

The course has be described in a very precise manner. The instructor takes time to clearly explain the concepts and the importance of the same.

By Filipe G

Apr 02, 2016

The best machine learning course I took online. I've taken other coursera courses, and this is the most complete, comprehensive, and well made.