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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: 176 - 200 / 453 レビュー

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.

by Ankur P

May 29, 2018

Loved the way our tutor (Carlos) explained the concepts to us. Things are getting clearer with each course in ML :) Many thanks :)

by Rodrigo T

Dec 30, 2017

Excellent course, i really like the general concepts

by Hansel G M

Nov 01, 2017

Great course !!! I totally recommend it.

by Suneel M

May 09, 2018

Excellent c

by Phil B

Feb 13, 2018

Excellent overview of the most commonly used Classification techniques, providing the wireframe for us to write our own algorithms from scratch. Really enjoyed this one.

by Norman O

Feb 19, 2018

I really liked this section on classification. Like with the regression course, complex concepts were explained well with nice examples and assignments. The only issue I had was that some of the coursework can be computing intensive (no surprise there). On the other hand, you really do learn by doing. And, of course, in the real world, computing resources (though plentiful) aren't infinite.

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 Roberto E

Mar 01, 2017


by Ian F

Jul 18, 2017

Good overview of classification. The python was easier in this section than previous sections (although maybe I'm just better at it by this point.) The topics were still as informative though!

by Saheed S

Jul 18, 2017

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

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 A S P

Nov 14, 2016

Informative with useful assignments and optional lectures that provide a deeper mathematical understanding. Great for newbies as well as more seasoned computer scientists looking to expand into new material.

by Navinkumar

Feb 23, 2017


by 海上机械师

Sep 13, 2016

So cool and much practical.

by Venkata D

Apr 14, 2016

Great course and learning

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 Xuan Q

Feb 14, 2017

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

by xiaofeng y

Feb 06, 2017


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.