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

4.7
2,883 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

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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226 - 250 of 447 Reviews for Machine Learning: Classification

By Sudip C

May 03, 2016

Very detailed, Liked optional sections also. Loved it.

By Marcio R

Jun 14, 2016

Curso excelente, desde o material, as atividades práticas e aulas. O fórum de discussões é repleto de pessoas interessadas em ajudar. Essa é a especialização a longa distância definitiva de Machine Learning.

By TONGHONG C

Jun 14, 2017

Best ML course I've ever taken!

By Rahul G

May 06, 2017

Excellent course except that week 7 th assignment based on ipynb notebook had some redundant questions. Otherwise a good course especially sheds light on Adaboost, ensemble classifiers and stochastic gradient with batch processing.

Thanks Professor Carlos.

By Etienne V

Nov 13, 2016

Great course with very good material! I'd like to see assignments that leaves more coding tasks to the student.

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 Paulo R M B

Jan 31, 2017

Well explaned !

By eric g

Mar 21, 2016

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

By Liang-Yao W

Aug 12, 2017

The course walk through (and work through) concepts of linear classifier, logistic regression, decision trees, boosting, etc. For me it is a good introduction to these fundamental ideas with depth but not too deep to be distracted.

I personally become interested in knowing a bit more theoretical basis of the tools or concepts like boosting or maximum likelihood. The course understandably doesn't go that much into math and theory which leaves me a bit unsatisfied :P. But that is probably too much to ask for a short course and I do think the course covers great materials already.

By Saransh A

Oct 31, 2016

Well this series just doesn't seize to amaze me! Another great course after the introductory and regression course. Though I really missed detailed explanations of Random Forest and other Ensemble methods. Also, SVM was not discussed, but there were many other topics which all other courses and books easily skips. The programming assignments were fine, more focused on teaching the algorithms than trapping someone in the coding part. This series is the series for someone who really wants to get a hold of what machine learning really is. One thing which I really like about this course is that there are optional videos from time to time, where they discuss the mathematical aspects of the algorithms that they teach. Which really quenches my thirst for mathematical rigour. Definitely continuing this specialisation forward

By Rajat S B

Jun 13, 2016

Great course , It gives the idea of how we should do classification from scratch as well as understanding the concept of how to handle large dataset during training. Boosting is one of the most important technique as what I have heard in machine learning and it's great to understand the concept of it.

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 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 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 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 Sanjay M

Jun 30, 2017

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

By Andrew M O

Jun 15, 2016

I came here to learn. I learned.

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 Sami A

May 20, 2016

The best in the field

By Ning Z

Mar 20, 2016

Great way of teaching, technical details well demystified. Thank you very much!

By Fernando B

Feb 21, 2017

Best Course on ML yet on the Web

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 Vijai K S

Mar 05, 2016

Heck yeah!! its finally here :D

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 Evgeni S

Jun 11, 2016

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