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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
3,563件の評価
589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 / 557 レビュー

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

by ANKAN M

2020年8月16日

nice

by Sadhiq A

2020年6月19日

good

by AMARTHALURU N K

2019年11月24日

good

by RISHI P M

2019年8月19日

Good

by Akash G

2019年3月10日

good

by xiaofeng y

2017年2月5日

good

by Kumiko K

2016年6月5日

Fun!

by Arun K P

2018年10月17日

G

by Navinkumar

2017年2月23日

g