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学生对 University of Washington 提供的 Machine Learning: Classification 的评价和反馈

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3,736 个评分

课程概述

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)....

热门审阅

JC

Mar 28, 2016

This continues UWash's outstanding Machine Learning series of classes, and is equally as impressive, if not moreso, then the Regression class it follows. I'm super-excited for the next class!

RB

Oct 19, 2020

This class was very interesting. I learned a lot. I really enjoyed the way the instructor presented the information. The programming assignments were challenging learning opportunities.

筛选依据：

151 - Machine Learning: Classification 的 175 个评论（共 589 个）

创建者 Hristo V

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Dec 1, 2016

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

创建者 Shaowei P

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Mar 30, 2016

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

创建者 Rashi K

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Mar 17, 2016

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

创建者 Dmitri T

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Apr 24, 2016

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

创建者 Deepak S

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Aug 21, 2020

Assignments are great providing an opportunity to have better understanding about the topic discussed

创建者 YASHKUMAR R T

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May 3, 2019

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

创建者 Jonathan C

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Jan 19, 2018

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

创建者 Yuexiu C

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Jan 20, 2017

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

创建者 Filipe L

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Oct 2, 2016

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

创建者 Evgeni S

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Jun 10, 2016

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

创建者 Patrick M

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Aug 8, 2016

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

创建者 Ayush K G

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Nov 1, 2017

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

创建者 Arslan a

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Feb 18, 2019

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

创建者 Evaldas B

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Dec 14, 2017

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

创建者 Aakash S

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Jun 14, 2019

Amazing Explanation of every thing related to Classification.

Thanks a lot for the course.

创建者 Viktor K

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May 14, 2021

I m learn many things in the coursera. This is one of the best app provide for everyone.

创建者 Gustavo d A C

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Apr 23, 2017

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

创建者 Mounika G

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May 3, 2020

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

创建者 Rahul M

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Nov 12, 2017

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

创建者 Kim K L

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Aug 13, 2016

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

创建者 Patrick A

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Jun 27, 2020

As usual, very simple way of explaining principles. Thanks very much for this course!

创建者 andreas c c

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Aug 16, 2017

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

The teachers are awesome!

创建者 Simon C

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Oct 28, 2016

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

创建者 Jifu Z

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Jul 22, 2016

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

创建者 Sanjay M

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Jun 30, 2017

Very nice course with good mix of machine learning concepts with maths, programming.