Chevron Left
Voltar para Machine Learning: Classification

Comentários e feedback de alunos de Machine Learning: Classification da instituição Universidade de Washington

4.7
estrelas
3,118 classificações
519 avaliações

Sobre o curso

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)....

Melhores avaliações

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

Filtrar por:

226 — 250 de {totalReviews} Avaliações para o Machine Learning: Classification

por Aditi R

Oct 20, 2016

Wonderful experience. Prof is very good.

por Manuel I C M

May 30, 2017

One of the best courses i've ever tried

por Garvish

Jun 14, 2017

Great Information and organised course

por Lei Q

Mar 16, 2016

Excellent theory and practice(coding)!

por MAO M

May 07, 2019

lots of work. very good for beginners

por Dhruvil S

Jan 10, 2018

Nice Course Clears a lot of concepts.

por Xue

Dec 15, 2018

Very good lessons on classification.

por Aayush A

Jul 16, 2018

very good course for classification.

por Colin B

Apr 09, 2017

Really interesting course, as usual.

por Jialie ( Y

Feb 08, 2019

It is really useful and up to date.

por Sean L

Aug 31, 2016

wonderful course for beginner of ML

por Cosmos D I

Mar 29, 2020

This course is very informational!

por Alessandro B

Oct 31, 2017

nice, clear engaging ...and useful

por 易灿

Nov 28, 2016

课程很生动,讲的很详细,真心谢谢导师!希望能在算法后面多提供点资料!

por Henry H

Nov 18, 2016

Very clear and easy to understand.

por Albert V d M

Mar 08, 2016

Very instructive, you learn a lot.

por Angel S

Mar 08, 2016

Awesome. Waiting for the next one.

por Jing

Aug 14, 2017

Better than the regression course

por Rishabh J

Dec 19, 2016

Amazing course, Amazing teaching.

por CHERUKURI S V N K

May 29, 2020

IT WAS EXCELLENT AND ENJOYED IT.

por Fernando B

Feb 21, 2017

Best Course on ML yet on the Web

por Pranas B

Jul 01, 2016

Good practice and bit of theory.

por Andrew M O

Jun 15, 2016

I came here to learn. I learned.

por zhenyue z

Jun 03, 2016

good lecture, good for everyone.

por Manuel T F

Jul 21, 2017

Really great course. Well done!