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Machine Learning: Classification, University of Washington

2,509 ratings
428 reviews

About this 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


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!


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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396 Reviews

By FanPingjie

Dec 09, 2018

useful and helpful course

By Nidal Madad Gbetoho

Dec 04, 2018

very good

By Srinivas CS

Dec 02, 2018

This course was really good and helped in understanding different techniques in Classification

By leonardo duarte

Dec 02, 2018

This course covered very interesting aspects of real-world applications for machine learning. From my point of view, the theory was very clear an valuable, until that point that the programming assignments closed the cycle beautifully.

By Sathiraju Eswar

Nov 28, 2018

It's such a well organized course. Concepts are taught in an interesting way and made simple to understand through examples that thread along the course. I would recommend any aspiring data scientists to take this course. Thank you Carlos and Emily.

By Javier Almansa

Nov 25, 2018

Quite Interesting. Entertaining and the lectures are quite easy to follow.

By Sacha van Weeren

Nov 10, 2018

The course is well structured and very well explained. The structure is step by step increasing the the complexity. The programming exercises are excellent. I really appreciate the humor and passion of Carlos in teaching the material and his ability to explain complex matters with simple examples. The only drawback is that the course uses python packages that are less familiar. That is why I audited the course and worked with pandas and sklearn.

By Fahad Sarfraz

Nov 03, 2018

The content was excellent and the exercises were really good. It would be better if svms and bayesian classifiers are also covered


Oct 30, 2018

Good learning

By Thomas Kramer

Oct 29, 2018

In my opinion, so far the best part in the specialization series. The only thing, that was strange for me is that the effort required for programming varied a lot. So from week to week, it was difficult to predict how much time and effort would be needed to finish the assignments in time.