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学生对 University of Washington 提供的 Machine Learning: Classification 的评价和反馈

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课程概述

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)....

热门审阅

ML

Mar 13, 2016

Great course!Personally I could use a little more on the math behind the algorithms (e.g. Adaboost, why does it work?).Also, would be great to add SVM in next iterations of this class.Thanks!

KL

Jun 23, 2017

Great course. I learned a lot about Classification theories as well as practical issues. The assignments are very informative providing complimentary understanding to the lectures.

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401 - Machine Learning: Classification 的 425 个评论（共 589 个）

创建者 Joshua C

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May 3, 2017

Awesome!

创建者 Roberto E

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Mar 1, 2017

awesome!

创建者 Isura N

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Dec 28, 2017

Hoooray

创建者 Anshumaan K P

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Nov 11, 2020

NYC ;)

创建者 Shashidhar Y

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Apr 2, 2019

Nice!!

创建者 Vyshnavi G

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Jan 23, 2022

Ossum

创建者 Md. T U B

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Sep 2, 2020

great

创建者 Subhadip P

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Aug 4, 2020

great

创建者 Nicholas S

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Oct 7, 2016

Great

创建者 李真

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Mar 5, 2016

great

创建者 Vemuri s s n s d s

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Jan 23, 2022

good

创建者 VYSHNAVI P

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Dec 13, 2021

GOOD

创建者 SAYANTAN N

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Oct 28, 2021

good

创建者 boulealam c

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Dec 15, 2020

good

创建者 Saurabh A

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Sep 10, 2020

good

创建者 SUJAY P

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Aug 21, 2020

nice

创建者 ANKAN M

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Aug 16, 2020

nice

创建者 Dr S J

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Jun 19, 2020

good

创建者 AMARTHALURU N K

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Nov 24, 2019

good

创建者 RISHI P M

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Aug 19, 2019

Good

创建者 Akash G

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Mar 10, 2019

good

创建者 xiaofeng y

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Feb 5, 2017

good

创建者 Kumiko K

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Jun 5, 2016

Fun!

创建者 Maram A A

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Dec 31, 2022

🙏

创建者 Arun K P

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Oct 17, 2018

G