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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)....



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


Machine Learning: Classification: 276 - 300 / 486 レビュー


May 24, 2020

Excellent Course.....

by Kaixiang Y

Jun 27, 2017

Very good instructors

by Sami A

May 20, 2016

The best in the field

by stephon_lu

Dec 23, 2017

very good! thank you

by Michael P

Dec 06, 2016

Awesome, not awful;)

by Jooho S

Jul 01, 2016

It's very practical.


Oct 14, 2019

Excellent tutorials

by Muhammad Z H

Aug 30, 2019

I have learned alot

by Luis E T N

Jul 04, 2017

Excelent! Congrats!

by Itrat R

Jan 23, 2017

Excellent Course!!!

by Roger S

Sep 04, 2016

This course is COOL

by Ankit S

Jun 08, 2016

Really nice course!

by Mrs. G A D

May 13, 2020

Wonderful learning

by Sandeep J

Sep 04, 2016

Its s great course

by Kurt K

Apr 16, 2016

Excellent course !

by Aparna g

Jan 30, 2020

very Good Concept

by Germanno T

Dec 04, 2019

Excellent Course!

by Miguel Á B P

May 21, 2019

Excellent course!

by parv j

Mar 03, 2019

Brilliant course!

by Mayank C

Apr 12, 2018

Loved this course

by Matt Y

Mar 10, 2018

Simply excellent!

by Jonathan H

Jun 16, 2017

Excellent course!

by Le D L

May 02, 2017

Lots of knowledge

by Prabal T

Oct 05, 2016

Excellent course!

by André F d A F C

Jul 25, 2016

Excellent course.