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Machine Learning: Classification, Université de Washington

4.7
2,606 notes
438 avis

À propos de ce cours

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)....

Meilleurs avis

par 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!

par 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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406 avis

par Arslan ahmad

Feb 18, 2019

the person who wants to start career in machine learning must take this course! Its awsome :)

par Zhongkai Mi

Feb 12, 2019

Great course, provided details that not show in others' and textbooks.

par Jialie (Julie) Yan

Feb 08, 2019

It is really useful and up to date.

par Ayswarya S

Feb 05, 2019

best course

par Satish Kumar Dewangan

Feb 03, 2019

it was easy to understand

par Mohit Garg

Feb 02, 2019

Good, insightful but repetitive coding.

par Manuel Gil

Jan 01, 2019

Really awesome course. Nice balance between practical uses, theory, and implementation projects. It's good they kept the "optional" videos for the more detailed discussion instead of just removing that material. Totally recommend it.

par Gaurav Gupta

Dec 27, 2018

Good Course!!

par Nitin Dobhal

Dec 18, 2018

Excellent lessons on this important topic Classification. I think all major areas were explained quite nicely, with proper examples.

par Xue

Dec 15, 2018

Very good lessons on classification.