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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

4.7
3,593件の評価
597件のレビュー

コースについて

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)....

人気のレビュー

SM
2020年6月14日

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 :)

SS
2016年10月15日

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!

フィルター:

Machine Learning: Classification: 376 - 400 / 566 レビュー

by Do A T

2017年11月15日

very useful

by Jinhui L

2016年9月1日

Good course

by Ayesha N

2021年7月19日

good stuff

by Deleted A

2020年5月3日

its useful

by Jan L

2017年8月2日

Just great

by 童哲明

2016年7月26日

very goog!

by Jair d M F

2016年4月21日

Very Good!

by Neha K

2020年9月19日

EXCELLENT

by PAWAN S

2020年9月17日

EXCELLENT

by Nidal M G

2018年12月4日

very good

by 王曾

2017年11月27日

very good

by Muhammad H S

2016年11月2日

Excellent

by Joshua C

2017年5月3日

Awesome!

by Roberto E

2017年3月1日

awesome!

by Isura N

2017年12月28日

Hoooray

by Anshumaan K P

2020年11月11日

NYC ;)

by Shashidhar Y

2019年4月2日

Nice!!

by Md. T U B

2020年9月2日

great

by Subhadip P

2020年8月4日

great

by Nicholas S

2016年10月7日

Great

by 李真

2016年3月5日

great

by SAYANTAN N

2021年1月28日

good

by boulealam c

2020年12月15日

good

by Saurabh A

2020年9月11日

good

by SUJAY P

2020年8月21日

nice