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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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301 - 325 of 447 Reviews for Machine Learning: Classification

By Vibhutesh K S

May 22, 2019

It was a very detailed course. I wished, doing it much earlier in my research career. Great insights and Exercises.

By akashkr1498

May 19, 2019

good course but make quize and assignment quize more understandable

By Karthik M

Jun 01, 2019

Excellent course and the instructors cover all the important topics

By Dohyoung C

Jun 04, 2019

Great ...

I learned quite a lot about classification

By Md s

Jun 09, 2019

awesome course , have learned lot of stuff

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 sudheer n

Jun 12, 2019

The way Carlos Guestrin explains things is exquisite. if basics is what is very important to you, and can learn code implementation and libraries from other sources, this is the go to course

By Yacine M T

Jul 31, 2019

Very helpful. Thank you

By Fan J

Aug 04, 2019

good content, help me a lot!

By Kevin

Aug 07, 2019

Great course for beginner to intermediate data science enthusiast! This course teaches you how to implement logistic regression, decision tree, AdaBoost algorithm, and stochastic approach from scratch! There's also some assignment to learn how to implement those algorithms in our preferred library. Would be great if Carlos & Emily can bring another advanced machine learning course!

By Hanna L

Aug 12, 2019

Great class!

By Muhammad W K

Aug 19, 2019

A great course. Starting from very simple and easy-to-understand concepts of classification, it takes us through very important grass-root concepts and algorithms necessary not only in classification but in better general machine learning understanding too. Like Precision and Recall, Boosting, Scalability and Online machine learning etc.

By RISHI P M

Aug 19, 2019

Good

By Miguel Á B P

May 21, 2019

Excellent course!

By Muhammad Z H

Aug 30, 2019

I have learned alot

By Parab N S

Oct 13, 2019

Excellent course on Classification by University of Washington

By AJAY K

Oct 14, 2019

Excellent tutorials

By Shrikrishna S P

Oct 18, 2019

The course is very well structured. It starts from the basic classifiers, further moving on to more complex ones. The instructors teach how to implement each mentioned algorithm from scratch, this really makes the course above par.

I loved the course and it helped me to become a good machine learning practitioner.

Thanks Emily and Carlos.

By Mohit G

Feb 02, 2019

Good, insightful but repetitive coding.

By Ayswarya S

Feb 05, 2019

best course

By Martin B

Apr 11, 2019

As with all the courses in this specialization: great production values, excellent tuition. Useful assignments, even though the reliance of Graphlab Create is a bit of a drag. I also would have liked to see some discussion of Support Vector Machines.

By ZhangBoyu

Jul 20, 2018

The lecturer speaks in a quite unclear manner, besides, everything is great and detailed.

By Lech G

Apr 26, 2016

Not as good as the Regression Course, but still very good.

While I appreciate prof Guestrin's enthusiasm, I missed a little rigor and mathematical depth of the Regression's course by prof. Fox.

I learned a lot, but I feel that regression clicked with me a little better than classification.

But that's probably me.

In either case, the whole series are awesome so far, better, in my opinion, than Anrdrew Ng's ML course on coursera,

A small suggestion would be to switch the main toolset from the Graphlab to something more common, like Sci-kit learn and Pandas.

By Shahin S

Sep 15, 2016

The lectures are very well prepared and clear. With regards to the assignments: I think it will be nice to design the assignments in a way that allows people to use the language and libraries they prefer as much as possible. I would also prefer to write more of the coding assignments by myself, instead of trying to fill in the blanks in some pre-written code and complete them. That will help the students to learn a lot more.

By Alberto B

Mar 17, 2018

Very good course