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Machine Learning: Classification に戻る

ワシントン大学(University of Washington) による Machine Learning: Classification の受講者のレビューおよびフィードバック



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)....



Jun 15, 2020

A very deep and comprehensive course for learning some of the core fundamentals of Machine Learning. Can get a bit frustrating at times because of numerous assignments :P but a fun thing overall :)


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!


Machine Learning: Classification: 326 - 350 / 540 レビュー

by André F d A F C

Jul 25, 2016

Excellent course.

by V S

Apr 28, 2016

Best course ever!

by Do H L

Mar 10, 2016


by Sukhvir S

Jul 10, 2020

Great Experience

by Phan T B

Apr 17, 2016

Very good course


Aug 01, 2020

best course....

by Manuel J U S

Jun 27, 2020

Awesome Course!

by Frank Z

Jul 04, 2018

Very good class

by Paulo R M B

Jan 31, 2017

Well explaned !

by Pandu R

Apr 20, 2016

Worth the wait.

by Roberto C

May 18, 2020

Simply amazing


May 05, 2020

Very well done

by Manan M

Apr 20, 2020

Amazing course

by Anuj k

Jul 19, 2020

great content

by Gaurav G

Dec 27, 2018

Good Course!!


Oct 30, 2018

Good learning

by Yang X

Oct 29, 2017

Very helpful!

by Omar B

Feb 09, 2017

Great course.

by Zizhen W

Nov 03, 2016

Pretty Solid!

by Manuel S

Sep 11, 2016

Great course!

by Aaqib M

Sep 20, 2020

Great course

by Manikant R

Jun 19, 2020

Great course

by Rekha R N

May 11, 2020

Nice course.

by Hanna L

Aug 12, 2019

Great class!

by Illia K

Oct 25, 2018

Very useful!