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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: 201 - 225 / 453 レビュー

by Uday A

Jun 15, 2017

Great learning experience. Thanks to Carlos and Emily! Loving every bit of this specialization. :)

It would help if there could be a small introduction to other types of classifiers (Naive Bayes, SVM etc), atleast pointing the student to external resources to try them out.

by Nicholas S

Oct 07, 2016


by Tuan L H

Dec 06, 2016

Great course, easy to follow, higly recommended!

by Richard N B A

Mar 09, 2016

A great course! Well presented, does not shy away from the mathematics (very nice optional units that go into more detail for the interested student!), keeps focus on the material and maintains the structure and feel of the specialization as a whole. It's great that we get to actually implement some of the algorithms. Strongly recommended!

by Thuong D H

Sep 23, 2016

Good course!

by Lixin L

May 07, 2017

really good course. thanks

by André F d A F C

Jul 25, 2016

Excellent course.

by David E

Aug 21, 2016

very useful course : covers a range of very practical and useful topics I had heard about but didn't fully understand until taking this course. Some highlights stochastic gradient, boosting, and precision-recall trade offs.

by Krzysztof S

Jun 06, 2017

great course

by Niyas M

Oct 29, 2016

Amazing course! Packed with insights, reasoning and Carlos's humor and wit. Highly recommended for novices (along with the Machine Learning Foundations course).

by Arash A

Dec 01, 2016

Learned a lot and enjoyed even more. Thanks!

by Lei Q

Mar 16, 2016

Excellent theory and practice(coding)!

by Nikolay C

Mar 16, 2016

Excellent course! I've learned these topics before, but many things were not clear enough. While learning this course my knowledge really improved a lot.

by Luis M

Jan 28, 2017

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


Aug 01, 2016

The course has be described in a very precise manner. The instructor takes time to clearly explain the concepts and the importance of the same.

by Vijai K S

Mar 05, 2016

Heck yeah!! its finally here :D

by Suoyuan S

Apr 21, 2016

This course is friendly to machine learning beginners for the learning material is easy to understand as well as the assignment is easy to accomplish.

by Fernando B

Feb 21, 2017

Best Course on ML yet on the Web

by Joshua C

May 03, 2017


by Binil K

Jul 30, 2016

Nice Course, very much helpful and reccomended

by Dhritiman S

Feb 09, 2017

These courses have been a perfect mix of theory and practice. Looking forward to the final two courses in the specialization getting released at some point in the future :)

by Mayank C

Apr 12, 2018

Loved this course

by Alexander S

Aug 07, 2016

one of the best courses.

by Usman

Nov 13, 2016

I think support vector machines is an important topic which is missing. Anyway, the programming assignments were terrific. I really enjoyed this course!

by Renato V

Jul 13, 2016

A very good course, with effective intuitive explanations of what the algorithms are supposed to achieve and how. The exercises in Python help understand the topic and fix it in memory.