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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: 251 - 275 / 453 レビュー

by Shuang D

Jun 29, 2018

nice course!

by Babak P

Jun 28, 2018

Great exposure that requires hand coding the algorithms. Really makes the concepts stick with a perfect combination of theory and programming mixed together.

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 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 Lixin L

May 07, 2017

really good course. thanks

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.

by Sami A

May 20, 2016

The best in the field

by Lei Q

Mar 16, 2016

Excellent theory and practice(coding)!

by Ning Z

Mar 20, 2016

Great way of teaching, technical details well demystified. Thank you very much!

by Fernando B

Feb 21, 2017

Best Course on ML yet on the Web

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 Benoit P

Dec 29, 2016

This whole specialization is an outstanding program: the instructors are entertaining, and they strike the right balance between theory and practice. Even though I consider myself quite literate in statistics and numerical optimization, I learned several new techniques that I was able to directly apply in various part of my job. We really go in depth: while other classes I've taken limit themselves to an inventory of available techniques, in this specialization I get to implement key techniques from scratch. Highly, highly recommended.

FYI: the Python level required is really minimal, and the total time commitment is around 4 hours per week.

by Andrew M O

Jun 15, 2016

I came here to learn. I learned.

by Pawan K S

May 15, 2016

Nice course with appropriate amount of detail in it! Covers tough mathematical aspect for those who are interested in it.

by Joshua A

Sep 20, 2016

Very thorough and engaging. Optional material allowed the more curious to learn a great deal about the topics. Simple, hands-on approach to classification algorithms. Highly recommended!

by Filipe G

Apr 02, 2016

The best machine learning course I took online. I've taken other coursera courses, and this is the most complete, comprehensive, and well made.

by Ganesan P

Feb 06, 2017

A very good course - understood a lot about classification and the understanding gained will help in reading text books like Ian Good Fellow for deep learning

by Jair d M F

Apr 21, 2016

Very Good!

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 Isaac B

Nov 20, 2016

Excellent course. Practical understanding of classification

by Manuel T F

Jul 21, 2017

Really great course. Well done!

by Wenxin X

Mar 26, 2016

This specialization overall is pretty good. Personally I feel like Classification talks more about concepts and important ideas and requires less on coding comparing to Regression. Learned a lot! Love Carlos and Emily!

by Maria C

Mar 08, 2016

One of the best online machine learning courses I have taken. Excellent explanation of many techniques on Classification. A great combination of theory and hands-on examples. Thank you, Professors Fox & Guestrin.