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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
2,976件の評価
490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

SS

Oct 16, 2016

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!

CJ

Jan 25, 2017

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses

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Machine Learning: Classification: 201 - 225 / 458 レビュー

by Trinh Q N

Jan 29, 2018

Give me a good understanding of Classification

by Anurag U

Jan 16, 2017

Best source to learn classification techniques

by Binil K

Jul 30, 2016

Nice Course, very much helpful and reccomended

by Arash A

Dec 01, 2016

Learned a lot and enjoyed even more. Thanks!

by 嵇昊雨

Apr 26, 2017

Great materials for learning Classification

by Kan C Y

Mar 19, 2017

Really a good course, succinct and concise.

by clark.bourne

May 09, 2016

Professional, comprehensive, worth to learn

by Md s

Jun 09, 2019

awesome course , have learned lot of stuff

by Fabiano B

Jul 21, 2017

It is a very good course. Congratulations!

by alireza r

May 29, 2017

It is really engaging and well explained.

by Ashley B

Nov 30, 2016

Great course. Material well presented and

by Abhishek T G

Jun 22, 2016

The quizzes can be a bit more challenging

by VITTE

Jul 18, 2018

Very clear and useful course, excellent.

by Hansel G M

Nov 01, 2017

Great course !!! I totally recommend it.

by Aditi R

Oct 20, 2016

Wonderful experience. Prof is very good.

by Manuel I C M

May 30, 2017

One of the best courses i've ever tried

by Garvish

Jun 14, 2017

Great Information and organised course

by Lei Q

Mar 16, 2016

Excellent theory and practice(coding)!

by MAO M

May 07, 2019

lots of work. very good for beginners

by Dhruvil S

Jan 10, 2018

Nice Course Clears a lot of concepts.

by Xue

Dec 15, 2018

Very good lessons on classification.

by Aayush A

Jul 16, 2018

very good course for classification.

by Colin B

Apr 09, 2017

Really interesting course, as usual.

by Jialie ( Y

Feb 08, 2019

It is really useful and up to date.

by Sean L

Aug 31, 2016

wonderful course for beginner of ML