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

2,908 個評分
482 個審閱


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)....



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!


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


151 - Machine Learning: Classification 的 175 個評論(共 450 個)

創建者 Dwayne E

Dec 21, 2016

Awesome course learned alot

創建者 Pranas B

Jul 01, 2016

Good practice and bit of theory.

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 Muhammad H S

Nov 02, 2016


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 clark.bourne

May 09, 2016

Professional, comprehensive, worth to learn

創建者 李今晖

Sep 01, 2016

Good course

創建者 Joseph F

Apr 05, 2018

Good course with many assignment to design the algorithm with your own code. But I think this course last a little bit too long.

創建者 Kurt K

Apr 16, 2016

Excellent course !

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

創建者 Kumiko K

Jun 05, 2016


創建者 Darryl L

Oct 27, 2016

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

創建者 Andre J

Mar 18, 2016

These Machine Learning classes have been fantastic so far, really enjoying them. Very good coverage of topics and challenging exercises to drive home the learning. The effort put into developing the classes has been superb and I look forward to the rest of the specialization.

創建者 ramesh

Mar 31, 2016

I come to know how can i applym machine learning conceps i real world scenarios . The instructors are so nice and always explaining in simple methods. Nice teaching abilities.. Glad to guided under this kind of instructors. Nice experience.

創建者 Syed A u R

Aug 11, 2016

exceptional course. Carlos did an excellenet job

創建者 Igor K

Mar 16, 2016

very interesting and novice friendly, however some math (basic matrix calculus and derivatives) review worth doing

創建者 Kuntal G

Nov 03, 2016

Great course with detail explanation ,hands-on lab along with some advance topic. Really a great course for anyone interested in the field of real world machine learning

創建者 Kan C Y

Mar 19, 2017

Really a good course, succinct and concise.

創建者 Thomas E

May 12, 2016

A bit easy to get through the exercises bur otherwise a very enlightening and inspiring course. - This is btw a positive review if anybody should be in doubt after taking this course :)

創建者 Jinho L

Jul 20, 2016

Very pragmatic and interesting

創建者 Leon A

Mar 10, 2016

Course material selection, pace and presentation are all well thought out. This sequence of courses in the Machine Learning specialization is truly exceptional.