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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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501 - Machine Learning: Classification 的 525 個評論(共 545 個)

創建者 Neelkanth S M

2019年4月8日

The content is good but completing assignments is a real pain because they choose to deploy a unstable proprietary python library, which gives hard time installing and running (as of Q1 2019). The entire learning experience is marred by this Graphlab python library.

創建者 Divya b

2018年6月13日

Pros: Absolutely fantastic theory explanations. Establishes solid fundamentals. Cons: The bugs in test/notebooks could have not been rectified with new ones. Demands searching in discussion forum every time. Would highly recommend for starters!

創建者 ANGELICA D C

2020年9月22日

Finalizo siendo muy confuso. El conocimiento de los videos opcionales no se le daba seguimiento, hasta el final en las tareas es cuando se usaba pero ya estaba fuera de contexto y era difícil entender.

創建者 Supharerk T

2016年7月6日

All of the courses lecture are great until it reaches week 5 where it's really hard to catch, the programming assignment doesn't give enough hints and lecture in this topic doesn't help much.

創建者 nazar p

2017年6月29日

While courses 1 and 2 of this specialization were quite good, I find this one a bit sparse on content. I think this course could be easily compressed into 2-3 weeks instead of 7.

創建者 Rohit J

2016年5月12日

A lot of interesting parts of the course are available as optional and a lot of the difficult parts of the coding exercises are provided to you - the challenge is not there. :/

創建者 Ilan S

2016年11月23日

The videos were pretty goods. But a bit too slow and easy. The assigments were ok, but too guiding. Also there were too much reimplementation of algorithm

創建者 Rahul S

2020年6月17日

Too much confusion, I face too much problem with this course. much confusion if you use different packages like sklearn.

創建者 Fengchen G

2016年5月18日

The course content seemed to be rushed out, as a result, the quality is not as good as the first two.

創建者 Tu L P H

2018年6月27日

Why don't you guys talk about ID3 or CART algorithm at all? This one is too basic.

創建者 Mounir

2016年6月19日

Exercises for Scikit-learn users were not organised.

Course took too long to start

創建者 Pier L L

2017年3月26日

Nice course but I would have expected more techniques (SVM for instance)

創建者 Dmitri B

2017年6月6日

Theory Quizes are good, but programming assignment not so good for me.

創建者 Ashish C

2019年3月31日

more topics like deep learning, neural networks need to be introduced

創建者 Matt T

2016年4月12日

Good, but overemphasizes niche software product (graphlab).

創建者 Virgil P

2018年2月18日

The exercises/assignments are far too simple

創建者 陈弘毅

2018年2月3日

too simple

創建者 Akash K S

2020年8月13日

good

創建者 Omkar v D

2018年8月14日

.

創建者 Rohan G L

2020年8月29日

I leave 2 stars as I learned a lot of new information and methods, and the theory and math behind them.

You will learn about Data Science and Machine Learning, but not much about Python.

The course is pretty much abandoned and outdated. Sframes and Turicreate packages (instructor's creations) are used instead of more universal packages. Installation in the beginning took some time and research. Many of the assignments have errors and bugs in the code that have not been updated. Forum assistance is abysmal for clarification or deeper questions. Many links are dead.

There are many times in the lectures where the instructors are writing several sentences in their handwriting on their notes instead of having the text ready to appear.

I would suggest using this course and series as a supplement to other information one as learned, not as an introduction for initial understanding. I found myself frustrated too many times.

創建者 Amit K

2018年1月20日

The video content is awesome. Important concepts are being clarified in a very simple manner. However the evaluation method really sucks. First, there is too much spoon feeding in the programming assignments, which was not the case in earlier courses in the same specialisation. Secondly, in a few assignments, the answer to the quiz questions are sensitive to the platform we are using (like PC vs AWS instance). This was really frustrating given that the issue is known for a long time and has not been fixed yet. At the very least, there should be a warning on the quiz page itself.

創建者 Yaron K

2016年9月30日

The assignments are well thought out and explain the algorithms step-by-step. The subtitles/transcripts are a disappointment :( . Full of mistakes. Sometimes to the point of being useless or even worse - saying the exact of opposite of what the lecturer says. Since the lecturer sometimes is unclear - this is problematic. As usual - Graphlab Create sometimes crashes, however there are explanations how to run the assignments using Scikit-Learn.

創建者 Matt B

2016年4月4日

The content seems rather thinner than that of earlier courses in the specialization, and seems to get more so as the course progresses. (Week 6 is entirely spent on Precision and Recall, with only about 30 min of lecture.) It feels like there was a rush to get the course out and that corners may have been cut at the end.

And as others have mentioned, several very important classification topics are conspicuously missing.

創建者 Alois H

2017年9月23日

Overall good explanations in the videos; however, too much reliance on GraphLab, so that it seems more like promotional course for the instructor's own software and company. Also, the course is generally a bit light on content - the only algorithms discussed are Logistic Regression, Decision Trees and AdaBoost. Spending a full week on precision & recall is way too much time.

創建者 john f

2020年7月20日

This course needs to be re-created using new professors.

Way too lazy IMO.

Too many "trick questions", total confusion between Python 2.x and 3.x

Too theoretical, almost no practical examples

Quizes are very poor and give no "hints" or true workhtrough examples pror to test.

This is a problem with all Coursera, though.