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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



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)....



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!


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


Machine Learning: Classification: 101 - 125 / 453 レビュー

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 Saheed S

Jul 18, 2017

It was a great course, I will start working on a new classification project. Thanks

by Pardha S M

Jun 02, 2017

All the quiz and programming assignments prepared such away that student can easily get into the workflow, concentrating more on concepts without taking much overhead of programming yet need to think rigorously while writing that small portion of "YOUR CODE" parts on couple of occasions

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 Josef H

Nov 27, 2016

I like the detailed comparison between choosing different parameters for creating the classification model. I learn a lot of tricks for creating plots.

by Dhruvil S

Jan 10, 2018

Nice Course Clears a lot of concepts.

by Daisuke H

May 18, 2016

I really love this Classification course as well as Regression course!! This course is covering both mathematical background and practical implementation very well. Assignments are moderately challenging and it was a very good exercise for me to have a good intuition about classification algorithms. I only used standard Python libraries such as numpy, scikit-learn, matplotlib and pandas, and there were no problems for me to complete all of the assignments without any use of IPython, SFrames, GraphLab Create at all. I would say thank you so much to Carlos and Emily to give me such a great course!!

P.S. This course would be perfect if it covered bootstrap and Random Forest in details.

by Venkata D

Apr 14, 2016

Great course and learning

by 海上机械师

Sep 13, 2016

So cool and much practical.

by 李紹弘

Aug 14, 2017

This course provides me the very clear concept.

by Bert B

Oct 20, 2016

Very well done course.

Would be nice to have many more very short examples during the lectures that match the formulas. This would help me understand the formulas much better since I do not have a calculus or linear algebra background.

by Frank Z

Jul 04, 2018

Very good class

by Evaldas B

Dec 14, 2017

Very nice course with a little bit of details about how classification is done. Enjoyed it.

by Konstantinos P

Mar 28, 2017

The context and the structure of the course is absolutely perfect. Also, Carlos is the perfect professor!

by japneet s c

Feb 06, 2018

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

by Do H L

Mar 10, 2016


by Carlos L

Jun 10, 2016

The contents are really clear and professors are great!

by Itrat R

Jan 23, 2017

Excellent Course!!!

by Krisda L

Jun 24, 2017

Great course. I learned a lot about Classification theories as well as practical issues. The assignments are very informative providing complimentary understanding to the lectures.

by Xuan Q

Feb 14, 2017

Super useful and a bit of challenging! Really enjoy it.

by Daniel Z

Mar 08, 2016

This is a hand-on very exciting course, strongly recommended for all audience

by Tewende J E K

Jul 24, 2016

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

by Jing

Aug 14, 2017

Better than the regression course

by Le L

May 02, 2017

Lots of knowledge

by ChangIk C

Oct 25, 2016

Learned a lot recommend!