Chevron Left
Volver a Machine Learning: Classification

Opiniones y comentarios de aprendices correspondientes a Machine Learning: Classification por parte de Universidad de Washington

4.7
estrellas
3,611 calificaciones
597 reseña

Acerca del 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)....

Principales reseñas

SM
14 de jun. de 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
15 de oct. de 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!

Filtrar por:

201 - 225 de 566 revisiones para Machine Learning: Classification

por Japneet S C

5 de feb. de 2018

Course is very good. Concepts are explained in a very simple way.

por dragonet

24 de mar. de 2016

thank you every much, every helpful! ~i will repeat several time~

por Mark W

6 de may. de 2017

Fantastic Lecturers and very interesting and informative course

por D D

16 de oct. de 2016

Nice videos. Learned a lot. Also videos good for future review.

por Eric N

11 de oct. de 2020

Excellent online teaching with clear and concise explanations!

por Parab N S

12 de oct. de 2019

Excellent course on Classification by University of Washington

por Mohd A

14 de ago. de 2016

Learning is fun when you have professors like Carlos Guestrin.

por Ali A

4 de sep. de 2017

the course material is great but the assignments are not good

por clara c

11 de jun. de 2016

This course was great! I really enjoyed it and learned a lot.

por Yufeng X

14 de jun. de 2019

The lecture is super. The exams could be more challenging-:)

por Sarah W

24 de sep. de 2017

Great course! Learned so much! So excited to use this stuff!

por Tony T

19 de nov. de 2016

funny and enthusiastic lecturer make a dry subject more fun.

por Simbarashe M

24 de sep. de 2020

l know a knew way to train the models taught in this course

por Isaac B

20 de nov. de 2016

Excellent course. Practical understanding of classification

por Ali A

21 de mar. de 2016

So far it is a mazing. I will rate at the end of the course

por Kartik W

19 de sep. de 2020

A must do course for all the machine learning enthusiasts.

por Koen O

14 de abr. de 2017

Excellent course for learning the basics on classification

por Chao L

31 de mar. de 2017

Nicely formatted. And it's quite intuitive and practical.

por Patrick P

28 de nov. de 2016

Very good and and informative to start with this subject.

por vacous

3 de ago. de 2017

very nice material covering the basic of classification.

por Xuan Q

13 de feb. de 2017

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

por Carlos L

10 de jun. de 2016

The contents are really clear and professors are great!

por Freeze F

7 de jun. de 2016

This lecture gave a great start for me into ML . :) :)

por Sudip C

3 de may. de 2016

Very detailed, Liked optional sections also. Loved it.

por Rodrigo T

30 de dic. de 2017

Excellent course, i really like the general concepts