Chevron Left
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
stars
2,949件の評価
486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: 376 - 400 / 454 レビュー

by Luiz C

Jun 07, 2018

Clear, good engaging videos, good quality/complexity balance of exercises

by Zebin W

Aug 24, 2016

It covers many aspects in clustering and the assignments are very helpful

by Luis d l O

Jun 22, 2016

Very easy to follow and didactic. Very good material in the assignments.

by Sander v d O

May 09, 2016

Simply a great course. Good intro to machine learning classifiation.

by Franklin W

May 04, 2017

Great beginner/advanced course for Machine Learning Classification!

by ELINGUI P U

Mar 07, 2016

Take you too long to come back, but the content is great. Good job

by Michael B

Sep 04, 2016

Good survey of the material, but assignments are superficial.

by SAI V L

Jan 26, 2018

Some instructions in programming assignments are not clear.

by charan S

Jul 30, 2017

Very nice course, detailed explanations and visualizations.

by Sahil M

Jul 10, 2018

Was a good course with some in-depth topics covered!

by Jiancheng

Mar 20, 2016

good course but too much easy, can be a good review.

by Hanqiao L

Aug 09, 2016

Need more content for SVM and Random Forest

by Alejandro T

Sep 09, 2017

It's a really good course, really liked it

by Mohit G

Feb 02, 2019

Good, insightful but repetitive coding.

by Sah-moo K

Apr 03, 2016

Decision trees and boosting were great.

by Gareth J

Aug 26, 2019

A good course to teach the key points.

by Hexuan Z

Oct 06, 2016

could be more challengable homework!!

by Vladislav V

May 13, 2016

It feels like it lacks certain depth.

by Farmer

Aug 12, 2018

Exercises are way too easy.

by Aadesh N

Jun 14, 2016

Great course materials

by Xiaojie Z

Jan 31, 2017

Can be more detailed.

by Ragunandan R M

Sep 17, 2018

Good overall course.

by Lim W A

Nov 21, 2016

Learnt new things.

by Mehul P

Aug 17, 2017

Nice explanation.

by gaozhipeng

Jul 01, 2016

good introduction