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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
3,103 個評分
518 條評論

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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126 - Machine Learning: Classification 的 150 個評論(共 486 個)

創建者 Etienne V

Nov 13, 2016

Great course with very good material! I'd like to see assignments that leaves more coding tasks to the student.

創建者 Naman M

Jul 09, 2019

you can't find a better course on machine learning as compared to this one. Simply the best course on coursera

創建者 Emil K

Jan 29, 2020

Such a great course. Brings the math behind machine learning to users without a math background. Thank you.

創建者 Naimisha S

Jul 30, 2018

Availability of the Ipython notebook makes it easy to solve the Quizzes which has step by step explaination

創建者 Konstantinos P

Mar 28, 2017

The context and the structure of the course is absolutely perfect. Also, Carlos is the perfect professor!

創建者 Hristo V

Dec 01, 2016

The course is absolutely amazing! Very clear explanation of the concepts with great notebook assignments.

創建者 Shaowei P

Mar 31, 2016

great course, would have been even more great if there are more details on how to use boosting for kaggle

創建者 Rashi K

Mar 17, 2016

Assignments were more challenging than previous course. Loved solving them. Enjoyed the optional videos.

創建者 Dmitri T

Apr 25, 2016

Really liked the practical application of this course - very useful in learning classification methods.

創建者 YASHKUMAR R T

May 03, 2019

This course will provide you clear and detailed explanation of all the topics of Classification.

創建者 Jonathan C

Jan 19, 2018

wow this was a good course. things got real here and hard. but I feel like I can do anything now

創建者 Yuexiu C

Jan 20, 2017

The instructor is awesome. He explained the boring statistical method in a very interesting way!

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 Evgeni S

Jun 11, 2016

Very focused overview of different classification methods. Goes deeper than in other ML classes.

創建者 Patrick M

Aug 08, 2016

Excellent course. Great mix of theory overview coupled with practical examples to work through.

創建者 Ayush K G

Nov 01, 2017

Usefull for getting ideas and depth knowledge in Classification. Explained in very simple way.

創建者 Arslan a

Feb 18, 2019

the person who wants to start career in machine learning must take this course! Its awsome :)

創建者 Evaldas B

Dec 14, 2017

Very nice course with a little bit of details about how classification is done. Enjoyed it.

創建者 Aakash S

Jun 15, 2019

Amazing Explanation of every thing related to Classification.

Thanks a lot for the course.

創建者 Gustavo d A C

Apr 23, 2017

It was a nice course. I could learn many new techniques and algorithms. Very exciting !!

創建者 Bheemagouni m

May 04, 2020

I have learnt many things from these course .This course helped me to learn from online

創建者 Rahul M

Nov 12, 2017

awesome course material to nourish your brain to classify in better decision making...

創建者 Kim K L

Aug 13, 2016

Another classic and fantastic. Love this Course and learn so much. Highly recommended!

創建者 andreas c c

Aug 16, 2017

The course is demanding but I learn a lot in classification.

The teachers are awesome!

創建者 Simon C

Oct 28, 2016

Great content and exercises which facilitated understanding of very complex concepts.