Chevron Left
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
星
3,523件の評価
•
586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： 176 - 200 / 554 レビュー

by James M

•
2016年7月20日

Top notch. Great course design. Best value for money in Machine Learning!

by Javier A

•
2018年11月25日

Quite Interesting. Entertaining and the lectures are quite easy to follow.

by Kazi N H

•
2016年6月23日

One of the awesome course on classification. Just so perfect for learning.

by Chandan D

•
2018年8月25日

I really enjoyed learning this course on Machine Learning Classification!

by Zuozhi W

•
2017年2月8日

Very informative class! The lectures are slow, clear, and easy to follow.

by Pankaj K

•
2017年9月25日

Great challenging and deep assignments! Big Thanks to both professors!!

by Zhongkai M

•
2019年2月12日

Great course, provided details that not show in others' and textbooks.

by courage s

•
2018年10月22日

Excellent Teaching with meticulous details and great humor. BIG Plus.

by Tewende J E K

•
2016年7月24日

intuitive, clear and practical. The best explanation I found so far !

by akashkr1498

•
2019年5月19日

good course but make quize and assignment quize more understandable

by Alexandre N

•
2016年12月20日

Excellent course with plenty of intuition and practical experiments.

by eric g

•
2016年3月21日

The best part for me in this specialization, Classification is great

by Swapnil A

•
2020年9月6日

Really awesome course. Dr. Carlos explains everything from scratch.

by Karthik M

•
2019年6月1日

Excellent course and the instructors cover all the important topics

by Srinivas J

•
2016年11月12日

truly enjoyed this course and recommended to my colleagues as well.

by Thierry Y

•
2017年11月12日

Great material, easy to follow, and nice examples around sushis :)

by Christian R

•
2017年9月11日

The visualizations provide deeper understanding in the algorithms.

by Luis M

•
2017年1月28日

Lots of practical tips, some applicabe not only to Classification.

by Yoshifumi S

•
2016年5月8日

As always in this specialization, tough course but so practical !!

by japneet s c

•
2018年2月5日

Course is very good. Concepts are explained in a very simple way.

by dragonet

•
2016年3月24日

thank you every much, every helpful! ~i will repeat several time~

by Mark W

•
2017年5月6日

Fantastic Lecturers and very interesting and informative course

by D D

•
2016年10月16日

Nice videos. Learned a lot. Also videos good for future review.

by Eric N

•
2020年10月11日

Excellent online teaching with clear and concise explanations!

by Parab N S

•
2019年10月12日

Excellent course on Classification by University of Washington

Coursera Footer

人気の高いオンラインコース

人気の高いオンライン専門講座

オンライン修了証

オンライン学位プログラム

Coursera

コミュニティ

さらに表示

場所を選ばす学習する
App StoreからダウンロードGoogle Playで取得