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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
2,991 個評分
493 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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SS

Oct 16, 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!

CJ

Jan 25, 2017

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses

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351 - Machine Learning: Classification 的 375 個評論(共 461 個)

創建者 KANDARP B S

Mar 02, 2017

The course 3 got pretty technical pretty soon. Enjoyed the first 2 courses without feeling overwhelmed. But course 3 was challenging. I suppose building the expectation of what is to come can reduce the challenge and lead to faster and more number of course completions.

創建者 Aleksander G

Apr 11, 2016

Just one comment about how the course could be improved: the assignments should be more hands-on with fewer pieces of code written in advance. I say this is even though I am not a skilled programmer. The assignments would be a bit harder, but also a bit more rewarding.

創建者 Jaime A C B

Sep 12, 2016

Sometimes is difficult to understand the concept behind Classification because some videos are more practical than theorical, I mean it could be better to start the video explaining some concepts and then show and explan some samples and theorical issues.

Thanks.

創建者 Nick S

Jan 02, 2020

The course itself is well structured and introduce gradually the complexity. Unfortunately, the exercises requires the use of a specific library, instead of scikit-learn and numpy. Furthermore, they also required Python 2, while Python 3 is now widely used.

創建者 Martin B

Apr 11, 2019

As with all the courses in this specialization: great production values, excellent tuition. Useful assignments, even though the reliance of Graphlab Create is a bit of a drag. I also would have liked to see some discussion of Support Vector Machines.

創建者 Uichong D L

Sep 17, 2017

Using discontinued Graphlab in the programming assignment is a minus and low activities in the forum makes hard to find assistance from the communities or mentors but the course material itself is just great.

創建者 João F A d S

Apr 18, 2016

Very good content, very well explained... great course. Classification its a very broad topic but i think this is great introduction.

The hands on where kinda on the easy side... but very interesting.

創建者 David F

Aug 07, 2016

Not as good as the previous courses in this specialization - I agree with those who have noted that this one seemed a little rushed. However, these are still the best courses I've found on Coursera.

創建者 Ahmed N

Feb 23, 2018

Great knowledge about machine learning fundamentals, More math illustration needed though it's great knowledge and very great basics about different machine learning algorithm used in reality

創建者 Eric M

Apr 15, 2017

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

Mar 20, 2017

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

Jun 28, 2019

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

May 01, 2018

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

Jun 10, 2016

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

Jun 12, 2016

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

Feb 07, 2020

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

Apr 22, 2016

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

Nov 03, 2018

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 Alexis C

Sep 29, 2016

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

創建者 Kishaan J

Jul 01, 2017

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

創建者 Raisa

Aug 19, 2017

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

創建者 Ning A

Sep 16, 2016

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

創建者 Yingnan X

Apr 14, 2016

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

創建者 Oleg R

Oct 09, 2016

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

創建者 Thrivikrama

Oct 12, 2016

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