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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
3,470 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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151 - Machine Learning: Classification 的 175 個評論(共 545 個)

創建者 Evgeni S

2016年6月10日

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

創建者 Patrick M

2016年8月8日

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

創建者 Ayush K G

2017年11月1日

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

創建者 Arslan a

2019年2月18日

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

創建者 Evaldas B

2017年12月14日

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

創建者 Aakash S

2019年6月14日

Amazing Explanation of every thing related to Classification.

Thanks a lot for the course.

創建者 Gustavo d A C

2017年4月23日

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

創建者 Bheemagouni m

2020年5月3日

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

創建者 Rahul M

2017年11月12日

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

創建者 Kim K L

2016年8月13日

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

創建者 Patrick A

2020年6月27日

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

創建者 andreas c c

2017年8月16日

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

The teachers are awesome!

創建者 Simon C

2016年10月28日

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

創建者 Jifu Z

2016年7月22日

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

創建者 Sanjay M

2017年6月30日

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

創建者 Saheed S

2017年7月18日

It was a great course, I will start working on a new classification project. Thanks

創建者 Darryl L

2016年10月27日

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

創建者 Ning Z

2016年3月20日

Great way of teaching, technical details well demystified. Thank you very much!

創建者 Michael O T

2019年11月29日

A great professor and a lot of knowledge about machine learning classification

創建者 Suresh K P

2017年12月19日

This course much helpful and understandable easily compared previous sessions.

創建者 Daopeng S

2016年4月12日

A very good introduce machine learning course, it's clear and easy to follow.

創建者 Daniel Z

2016年3月8日

This is a hand-on very exciting course, strongly recommended for all audience

創建者 Vladimir V

2017年6月14日

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

創建者 James M

2016年7月20日

Top notch. Great course design. Best value for money in Machine Learning!

創建者 Javier A

2018年11月25日

Quite Interesting. Entertaining and the lectures are quite easy to follow.