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

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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


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 :)


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!

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226 - 250 de 575 revisiones para Machine Learning: Classification

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 Tarek M A A

15 de feb. de 2022

Full of useful, good put simple explanation for each

por Rodrigo T

30 de dic. de 2017

Excellent course, i really like the general concepts

por susmitha

5 de ago. de 2020

Very clear and good explanation by both instructors

por Dohyoung C

3 de jun. de 2019

Great ...

I learned quite a lot about classification

por Maxwell N M

7 de oct. de 2018

Great Course!

Teachers are genius and awesome


por Norberto S

9 de oct. de 2016

Excellent course with lots of practical exercises.

por JOSE R

18 de nov. de 2017

Very interesting. It's easy to understand. Thanks

por Tuan L H

6 de dic. de 2016

Great course, easy to follow, higly recommended!

por Syed A R

11 de ago. de 2016

exceptional course. Carlos did an excellenet job

por Mariano

4 de abr. de 2020

very interesting and useful tools for real life

por 李紹弘

14 de ago. de 2017

This course provides me the very clear concept.

por LIU Y

22 de mar. de 2016

best of the best, theoretically and practically


22 de ago. de 2018

The course is good. The materials are amazing!

por Trinh N Q

28 de ene. de 2018

Give me a good understanding of Classification

por Anurag U

16 de ene. de 2017

Best source to learn classification techniques

por Binil K

30 de jul. de 2016

Nice Course, very much helpful and reccomended


21 de mar. de 2020

Very good programming assignments. Loved it.

por Arash A

30 de nov. de 2016

Learned a lot and enjoyed even more. Thanks!

por 嵇昊雨

25 de abr. de 2017

Great materials for learning Classification

por Kan C Y

19 de mar. de 2017

Really a good course, succinct and concise.

por clark.bourne

8 de may. de 2016

Professional, comprehensive, worth to learn

por Steve F S

24 de jun. de 2020

challenging course for any non-math major.