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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
3,554件の評価
588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: 451 - 475 / 556 レビュー

by Thrivikrama

2016年10月12日

Good course.. Should have SVM related info too -- waiting for the promised optional videos from Prof. Carlos

by Tomasz J

2016年4月4日

Great course! However I put only 4 starts because I would like to see random forests which are not present.

by Baubak G

2018年6月10日

I think the course on boosting could be worked on better. But all in all I really enjoyed this course.

by Simon C

2020年5月1日

It's still a great course. But I think the quality of the regression one is better than this overall.

by Srinivas C

2018年12月2日

This course was really good and helped in understanding different techniques in Classification

by Sapna A

2021年2月2日

The course was awesome, especially with sentimental classification case explanation... Thanks

by ZhangBoyu

2018年7月20日

The lecturer speaks in a quite unclear manner, besides, everything is great and detailed.

by Shashank A

2020年6月9日

Overall good, But it seems like same type of questions are repeated in assignment quiz

by Rattaphon H

2016年8月13日

The questions are hard to understand and ambiguous though their answers are easy.

by Bruno G E

2016年4月17日

Lack some of classical classification algorithms like SVM and Neural Netwroks.

by Jacob M L

2016年6月24日

Very approachable material, given the diversity of classification algorithms.

by hiram y s

2020年4月26日

Very well explained and with careful guidance through the programming steps.

by Luiz C

2018年6月7日

Clear, good engaging videos, good quality/complexity balance of exercises

by Zebin W

2016年8月24日

It covers many aspects in clustering and the assignments are very helpful

by Luis d l O

2016年6月22日

Very easy to follow and didactic. Very good material in the assignments.

by Sander v d O

2016年5月9日

Simply a great course. Good intro to machine learning classifiation.

by Franklin W

2017年5月4日

Great beginner/advanced course for Machine Learning Classification!

by Pascal U E

2016年3月7日

Take you too long to come back, but the content is great. Good job

by Michael B

2016年9月4日

Good survey of the material, but assignments are superficial.

by SAI V L

2018年1月26日

Some instructions in programming assignments are not clear.

by charan S

2017年7月30日

Very nice course, detailed explanations and visualizations.

by Sahil M

2018年7月10日

Was a good course with some in-depth topics covered!

by Jiancheng

2016年3月20日

good course but too much easy, can be a good review.

by Hanqiao L

2016年8月9日

Need more content for SVM and Random Forest

by Alejandro T

2017年9月9日

It's a really good course, really liked it