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

4.7
2,841 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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126 - 150 of 443 Reviews for Machine Learning: Classification

By Freeze F

Jun 07, 2016

This lecture gave a great start for me into ML . :) :)

By Aditi R

Oct 20, 2016

Wonderful experience. Prof is very good.

By Colin B

Apr 09, 2017

Really interesting course, as usual.

By venkatpullela

Nov 17, 2016

Course is really good. Assignments are taking too much time if you want to do the course rally fast, with questionable learning value.

By Phil B

Feb 13, 2018

Excellent overview of the most commonly used Classification techniques, providing the wireframe for us to write our own algorithms from scratch. Really enjoyed this one.

By Rahul M

Nov 12, 2017

awesome course material to nourish your brain to classify in better decision making...

By Trinh Q N

Jan 29, 2018

Give me a good understanding of Classification

By Do A T

Nov 15, 2017

very useful

By Siddharth S

Jan 09, 2018

Excellent course and all the concepts have been explained very simply and with an element of fun.

Many thanks to Emily and Carlos...

By JOSE R

Nov 18, 2017

Very interesting. It's easy to understand. Thanks

By Shuang D

Jun 29, 2018

nice course!

By Dongliang Z

Mar 22, 2018

Excellent course! The teacher explained a lot of intuitions during the course. The optional part s are very interesting and helpful.

By Yang X

Oct 29, 2017

Very helpful!

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 D D

Oct 16, 2016

Nice videos. Learned a lot. Also videos good for future review.

By Abhishek T G

Jun 22, 2016

The quizzes can be a bit more challenging

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 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 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 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 Ankit S

Jun 08, 2016

Really nice course!

By Luis E T N

Jul 04, 2017

Excelent! Congrats!

By Dwayne E

Dec 21, 2016

Awesome course learned alot