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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

SM

Jun 14, 2020

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

Oct 15, 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!

筛选依据：

126 - Machine Learning: Classification 的 150 个评论（共 589 个）

创建者 Sergio D H

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Jul 22, 2016

AWESOME COURSE!! Carlos and Emily are incredible teachers and the course contents are truly informative and well-paced for beginners.

创建者 stephane d

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Feb 20, 2021

Really a great course!

Thanks to Emily and Carlos!

I still hope there will be more courses after the 4 courses of this specialization.

创建者 Nitin D

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Dec 18, 2018

Excellent lessons on this important topic Classification. I think all major areas were explained quite nicely, with proper examples.

创建者 Dongliang Z

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Mar 22, 2018

Excellent course! The teacher explained a lot of intuitions during the course. The optional part s are very interesting and helpful.

创建者 Ornella G

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Oct 1, 2016

I really enjoyed the topics presented and the fluid way to present them. It's a very well done summary of the classification models.

创建者 Siddharth S

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Jan 8, 2018

Excellent course and all the concepts have been explained very simply and with an element of fun.

Many thanks to Emily and Carlos...

创建者 Alvin K

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Sep 27, 2020

This was a very great course. I got the confidence to use ML algorithms and concepts efficiently and also write my own algorithms.

创建者 Gaurav C

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May 22, 2019

Would have loved even more had Carlos explained his students gradient boosting as well. I liked the way of his taught in lectures.

创建者 Ankur P

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May 28, 2018

Loved the way our tutor (Carlos) explained the concepts to us. Things are getting clearer with each course in ML :) Many thanks :)

创建者 Renato R S

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Aug 27, 2016

All the basics - and much of the advanced stuff - is presented, in a coherent and inspired way. Thanks for crafting such a course.

创建者 Joseph F

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Apr 5, 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.

创建者 Tanachote R

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Apr 25, 2020

Thank you for sharing your knowledge to me. This course is very good and I really appreciate both of you (Carlos and Emily)

创建者 Reinhold L

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Mar 21, 2019

Very good course for classification in machine learning - top presentation documents - very well structured and practical

创建者 Pawan K S

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May 15, 2016

Nice course with appropriate amount of detail in it! Covers tough mathematical aspect for those who are interested in it.

创建者 Fabio P

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Apr 18, 2016

Very interesting topic with some advanced topics covered. It really shows how to use machine learning in the real world.

创建者 Matthew S

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May 22, 2020

Great! Not horribly wretchedly awful, but actually very good! (With this class I hope this is classified correctly!)

创建者 Vibhutesh K S

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May 22, 2019

It was a very detailed course. I wished, doing it much earlier in my research career. Great insights and Exercises.

创建者 Igor K

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Mar 16, 2016

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

创建者 Etienne V

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Nov 13, 2016

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

创建者 Naman M

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Jul 9, 2019

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

创建者 Divyang S

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Sep 13, 2020

Excellent and very in-depth coverage of basic and advanced concepts... Perhaps the best course out there !!!!

创建者 Emil K

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Jan 29, 2020

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

创建者 Naimisha S

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Jul 30, 2018

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

创建者 Hamed A

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Jul 26, 2022

Best machine learning course I have had so far! (guess this includes great words for a fantastic review)

创建者 Konstantinos P

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Mar 28, 2017

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