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學生對 华盛顿大学 提供的 Machine Learning: Classification 的評價和反饋

4.7
2,902 個評分
482 個審閱

課程概述

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)....

熱門審閱

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

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26 - Machine Learning: Classification 的 50 個評論(共 450 個)

創建者 Chandan D

Aug 25, 2018

I really enjoyed learning this course on Machine Learning Classification!

創建者 Arun K P

Oct 17, 2018

G

創建者 Fakrudeen A A

Sep 15, 2018

Excellent course - teaches linear, logistic regression and decision trees. It also teaches the most important concept of precision-recall. Overall highly recommended.

創建者 Maxwell N M

Oct 07, 2018

Great Course!

Teachers are genius and awesome

Thanks

創建者 Courage S

Oct 22, 2018

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

創建者 Illia K

Oct 25, 2018

Very useful!

創建者 Thomas K

Oct 29, 2018

In my opinion, so far the best part in the specialization series. The only thing, that was strange for me is that the effort required for programming varied a lot. So from week to week, it was difficult to predict how much time and effort would be needed to finish the assignments in time.

創建者 SANKARBALAJI M

Oct 30, 2018

Good learning

創建者 Aayush A

Jul 16, 2018

very good course for classification.

創建者 Pankaj K

Sep 25, 2017

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

創建者 D D

Oct 16, 2016

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

創建者 Vladimir V

Jun 14, 2017

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

創建者 Sandeep K S

May 07, 2016

awesome course awesome teachers

創建者 Henry H

Nov 18, 2016

Very clear and easy to understand.

創建者 clark.bourne

May 09, 2016

Professional, comprehensive, worth to learn

創建者 B M K

Oct 16, 2016

Challenging and Exciting Course. Lots of ML concepts (Decision Trees, AdaBoost, Ensembles, Stochastic gradient, loglikelyhood etc. ) are introduced and i believe this course is of extreme importance in laying the fundamentals of ML.

創建者 Muhammad H S

Nov 02, 2016

Excellent

創建者 Sergio D H

Jul 22, 2016

AWESOME COURSE!! Carlos and Emily are incredible teachers and the course contents are truly informative and well-paced for beginners.

創建者 Norberto S

Oct 09, 2016

Excellent course with lots of practical exercises.

創建者 嵇昊雨

Apr 26, 2017

Great materials for learning Classification

創建者 Filipe P L

Oct 03, 2016

Very good, sometimes is a little hard, but is very helpful and have a lot of practical exercises

創建者 Jifu Z

Jul 23, 2016

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

創建者 Kumiko K

Jun 05, 2016

Fun!

創建者 Shanchuan L

Dec 07, 2016

This is a perfect course

創建者 LIU Y

Mar 22, 2016

best of the best, theoretically and practically