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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: 126 - 150 / 440 レビュー

by Ali A

Mar 21, 2016

So far it is a mazing. I will rate at the end of the course

by Andrea C

Sep 07, 2016

The course covers most important topics in depth and exercises are very interesting, them helps you to reason about some important theoretical concepts.

by 童哲明

Jul 27, 2016

very goog!

by Apurva A

Jun 14, 2016

This course is very nice and covers some of the very important concepts like decision trees, boosting, and online learning apart form logistic regression. More importantly, everything here has been implemented from scratch and so the understanding of codes becomes very easy.

The lectures and slides were very intuitive. Carlos has explained everything very properly and even some of the very tough concepts have been explained in a proper manner from figures and graphs.

There are lots lots of python assignments to review what have we learned in the lectures.

Overall, its a must take course for all who wants an insight about classification in ML.

by Do H L

Mar 10, 2016

Awesooommmmeeeeee

by Mohd A

Aug 14, 2016

Learning is fun when you have professors like Carlos Guestrin.

by dragonet

Mar 24, 2016

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

by Chao L

Mar 31, 2017

Nicely formatted. And it's quite intuitive and practical.

by Theodore G

Oct 21, 2016

An interesting series of Lectures in the important topic of Classification. The business case approach followed by the instructors provides great help to apply the required theoretical knowledge and further elaborate these methods.

by Gunjari B

May 21, 2018

An absolute marvel of a course! In depth explanation to everything, detailed and important concepts explained so much at ease with Carlos' humour!

by Rajkumar K

Apr 01, 2017

This is a great course on ML - Classification that introduces one to the various techniques available in classification and to understand the algorithm under the hood. The course also explains the process, approach for each technique along with the methods to evaluate the results. Overall this takes the student through the next steps of learning classification algorithm from the foundational courses.

by ChangIk C

Oct 25, 2016

Learned a lot recommend!

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 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 Garvish

Jun 14, 2017

Great Information and organised course

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 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 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 Rahul M

Nov 12, 2017

awesome course material to nourish your brain to classify in better decision making...

by Yang X

Oct 29, 2017

Very helpful!

by Hugo L M

May 18, 2018

Very nice feelings from this course. Nice teacher, nice contents and very nice assignements, everything very well structured. As you can see the sentiment coming from my review is a clear +1, so I hope the algorithm looking for good reviews to show to other posible students chooses mine to show up!

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