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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

4.7
3,266件の評価
540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

SM

Jun 15, 2020

A very deep and comprehensive course for learning some of the core fundamentals of Machine Learning. Can get a bit frustrating at times because of numerous assignments :P but a fun thing overall :)

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!

フィルター:

Machine Learning: Classification: 151 - 175 / 510 レビュー

by Kim K L

Aug 13, 2016

Another classic and fantastic. Love this Course and learn so much. Highly recommended!

by Patrick A

Jun 27, 2020

As usual, very simple way of explaining principles. Thanks very much for this course!

by andreas c c

Aug 16, 2017

The course is demanding but I learn a lot in classification.

The teachers are awesome!

by Simon C

Oct 28, 2016

Great content and exercises which facilitated understanding of very complex concepts.

by Jifu Z

Jul 23, 2016

Good class, But it would be much better if the quiz is open to those who doesn't pay.

by Sanjay M

Jun 30, 2017

Very nice course with good mix of machine learning concepts with maths, programming.

by Saheed S

Jul 18, 2017

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

by Darryl L

Oct 27, 2016

they do a good job explaining concepts in great detail so everyone can learn it.

by Ning Z

Mar 20, 2016

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

by Michael O T

Nov 30, 2019

A great professor and a lot of knowledge about machine learning classification

by Suresh K P

Dec 19, 2017

This course much helpful and understandable easily compared previous sessions.

by Daopeng S

Apr 12, 2016

A very good introduce machine learning course, it's clear and easy to follow.

by Daniel Z

Mar 08, 2016

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

by Vladimir V

Jun 14, 2017

Awesome course! Highly recommend for anyone interested in machine learning.

by James M

Jul 20, 2016

Top notch. Great course design. Best value for money in Machine Learning!

by Javier A

Nov 25, 2018

Quite Interesting. Entertaining and the lectures are quite easy to follow.

by kazi n h

Jun 23, 2016

One of the awesome course on classification. Just so perfect for learning.

by Chandan D

Aug 25, 2018

I really enjoyed learning this course on Machine Learning Classification!

by Zuozhi W

Feb 09, 2017

Very informative class! The lectures are slow, clear, and easy to follow.

by Pankaj K

Sep 25, 2017

Great challenging and deep assignments! Big Thanks to both professors!!

by Zhongkai M

Feb 12, 2019

Great course, provided details that not show in others' and textbooks.

by Courage S

Oct 22, 2018

Excellent Teaching with meticulous details and great humor. BIG Plus.

by Tewende J E K

Jul 24, 2016

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

by akashkr1498

May 19, 2019

good course but make quize and assignment quize more understandable

by Alexandre N

Dec 20, 2016

Excellent course with plenty of intuition and practical experiments.