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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
3,471 個評分
577 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)....

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SM
2020年6月14日

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 :)

SS
2016年10月15日

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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426 - Machine Learning: Classification 的 450 個評論(共 545 個)

創建者 Eric M

2017年4月15日

Extremely clear and informative. Good introduction to ML. I felt the labs could have had us write a little more of our own code, and would have been better to use non-proprietary libraries.

創建者 Dawid L

2017年3月20日

Presented content is rather clear and instructors are rather easy to follow. Only the assignments are often confusing as there are questions which refer to missing content.

創建者 Thuc D X

2019年6月27日

Sometimes the assignment description was hard to follow along. Overall, the course equips me a good understand and practical skills to tackle classification tasks.

創建者 Gaurav K J

2018年5月1日

I learnt a lot, but I feel course 2 was very well made and this one felt a bit unstructured in comparison. Also, assignments in this course were made very easy.

創建者 Justin K

2016年6月10日

Assignments were a little too easy, considering that students are expected to have taken the first two courses in the specialization. Otherwise, great course!

創建者 Hao H

2016年6月12日

Good course overall. Some difficult materials such as boosting were not clear enough and I had to look into a few online resources to really understand it.

創建者 Fangzhe G

2020年2月7日

This course could be better if more programming content was taught. The programming assignments are difficult and not taught in courses.

創建者 Brian B

2016年4月22日

Great course. I'm really looking forward to learn more about clustering in the next course since I know nearly nothing about clustering.

創建者 Fahad S

2018年11月3日

The content was excellent and the exercises were really good. It would be better if svms and bayesian classifiers are also covered

創建者 Aaron

2020年7月3日

Nice course for new learner of machine learning, but I do hope this course could have introduction to support vector machine.

創建者 Alexis C

2016年9月29日

wanted more sophisticated mathematics and intuition (as opposed to simpler explanations). [regression course had this ...]

創建者 Kishaan J

2017年7月1日

Really loved this course! The insights into decision trees and precision-recall couldn't have been any better! Thank you!

創建者 Raisa

2017年8月19日

Wanted some stuff on SVM and Dimensionality Reduction. Awaiting for a course on Recommender Systems and Deep Learning

創建者 Ning A

2016年9月16日

Learn more than just classification, but also learn how to understand the ideas behind classification algorithms.

創建者 Yingnan X

2016年4月14日

A good course to start learning classifications and getting exposure to algorithms. The instructor is awesome!!

創建者 Oleg R

2016年10月9日

I would prefer more complex assignments and more advanced math concepts in the course. Otherwise it is great.

創建者 Thrivikrama

2016年10月12日

Good course.. Should have SVM related info too -- waiting for the promised optional videos from Prof. Carlos

創建者 Tomasz J

2016年4月4日

Great course! However I put only 4 starts because I would like to see random forests which are not present.

創建者 Baubak G

2018年6月10日

I think the course on boosting could be worked on better. But all in all I really enjoyed this course.

創建者 Simon C

2020年5月1日

It's still a great course. But I think the quality of the regression one is better than this overall.

創建者 Srinivas C

2018年12月2日

This course was really good and helped in understanding different techniques in Classification

創建者 ZhangBoyu

2018年7月20日

The lecturer speaks in a quite unclear manner, besides, everything is great and detailed.

創建者 Shashank A

2020年6月9日

Overall good, But it seems like same type of questions are repeated in assignment quiz

創建者 Rattaphon H

2016年8月13日

The questions are hard to understand and ambiguous though their answers are easy.

創建者 Bruno G E

2016年4月17日

Lack some of classical classification algorithms like SVM and Neural Netwroks.