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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
2,824件の評価
472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

フィルター:

Machine Learning: Classification: 151 - 175 / 441 レビュー

by Patrick P

Nov 28, 2016

Very good and and informative to start with this subject.

by Koen O

Apr 14, 2017

Excellent course for learning the basics on classification

by Fakhre A

Feb 17, 2017

Outstanding Course.....

by Prajna P

Dec 18, 2017

I enjoyed this course a lot. The case study approach and the optional videos are full of intuitions and I love the way instructors put across the concepts very clearly ... Thank you so much

by Abhijit P

Oct 25, 2017

Excellent course. Loved getting into the details of classification. This was a bit loaded with couple of quizzes as well as assignments in each module. Some questions were tricky and had to go through the videos again to figure out the correct answer. Carlos explained all the concepts very well

by ZHE C

Mar 26, 2017

effective teaching and practice about decision tree, boosting, and logistic regression. Could have a little more practice on gradient boosted tree/random forest

by Ashley B

Nov 30, 2016

Great course. Material well presented and

by Jonathan C

Jan 19, 2018

wow this was a good course. things got real here and hard. but I feel like I can do anything now

by Dongliang Z

Mar 22, 2018

Excellent course! The teacher explained a lot of intuitions during the course. The optional part s are very interesting and helpful.

by Ferenc F P

Jan 18, 2018

This is a very good course in classification. Starts with logistic regression (w. and wo. regularization) and then makes a very good introduction to decision trees and boosting. Also has a very good explanation about stochastic gradient descent. The only drawback is that for some Quizes the result is different with scikit-learn than with Graphlab while the Quiz is prepared for Graphlab results. Thus, with scikit-learn one may fail some of them.

by Fabiano B

Jul 21, 2017

It is a very good course. Congratulations!

by Mark h

Jul 27, 2017

Very Helpful Material!!!

by alireza r

May 29, 2017

It is really engaging and well explained.

by Michele P

Aug 23, 2017

The course starts slow, but it gets more interesting from week 2. The assignments are more challenging than in Regression, but I have really enjoyed it. I highly recommend it!

by Garvish

Jun 14, 2017

Great Information and organised course

by Nguyen D P

Dec 20, 2017

This course is so good. I can understand the algorithm and know the way how i can apply this for real life. Thanks so much coursera.org and Washinton university made the wonderful job for everybody. After this course i changed vision, innovation and i think people like me.

by Jonathan H

Jun 16, 2017

Excellent course!

by Suresh K P

Dec 19, 2017

This course much helpful and understandable easily compared previous sessions.

by 童哲明

Jul 27, 2016

very goog!

by zhenyue z

Jun 03, 2016

good lecture, good for everyone.

by Brian N

May 20, 2018

Nice to learn this topic

by kumar A

Jun 05, 2018

great course for beginners

by stephon_lu

Dec 23, 2017

very good! thank you

by Thierry Y

Nov 12, 2017

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

by Sean S

Mar 09, 2018

I am generally very happy with the style, pace, and content of this entire specialization. This course is no exception and exposed me to a lot of new concepts and helped me to improve my python programming skills. I am left wondering if the programming assignments were made easier over time given all of the hints and "checkpoints" for code that was already supplied. I understand this is not a programming course but I probably would have been okay with toiling away at the algorithms for a few more hours without the hints. But that's just me. Great course.