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Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Classification von University of Washington

3,523 Bewertungen
586 Bewertungen

Über den Kurs

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)....


14. Juni 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 :)

15. Okt. 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!

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476 - 500 von 554 Bewertungen für Machine Learning: Classification

von Chitrank G

10. Mai 2020

The course is excellent for beginners.

von Gareth W J

26. Aug. 2019

A good course to teach the key points.

von Hexuan Z

6. Okt. 2016

could be more challengable homework!!

von Vladislav V

13. Mai 2016

It feels like it lacks certain depth.

von S G

22. Mai 2020

Course material can be much better

von Farmer

12. Aug. 2018

Exercises are way too easy.

von Aadesh N

13. Juni 2016

Great course materials

von Xiaojie Z

31. Jan. 2017

Can be more detailed.

von Ragunandan R M

17. Sep. 2018

Good overall course.

von 2K18/SE/035 A K

11. Nov. 2020

content is complete

von Lim W A

21. Nov. 2016

Learnt new things.

von Mehul P

17. Aug. 2017

Nice explanation.

von gaozhipeng

30. Juni 2016

good introduction

von Alberto B

17. März 2018

Very good course

von Antonio P L

30. Apr. 2016

Fantastic Course

von Anand B

7. Aug. 2017

Great course!


3. Dez. 2020

good course.

von Ayswarya S

5. Feb. 2019

best course

von Alberto J L R

12. Okt. 2017

Good Mooc

von Syamsul B

31. Aug. 2020



23. Aug. 2019


von Serge B

2. Juli 2016



20. Mai 2018


von Ole H S

16. Juni 2016

First. I like these courses allot. They are pretty close to covering just what you need to actually do machine learning in the real world and not dive too deep into topics that have no practical value.


This course was a bit too thin, the last 4 weeks of the course contained little in depth informations and seemed to brush over allot of different topics that could have contained more information. Although they where important topics the course could go more in depth on at least 3 or 4 of those topics. The last 3 weeks could have been a course on its own if properly explored. However the concepts are well enough covered to be usable in practice i belive.

The programming exercises where ridiculously simple. Everything was reduced to filling in 1 or two lines in a bigger function. I understand that the point was to see how these functions are made and that it increases our understanding of the algorithms already existing in packages like schikit-learn and graphlab. Also the content became a bit too repetetive (actually started in the second course but continues in this course). The time used on variation over the same topic in different models made it challenging to pay attention when the lecture finally came to a new point (brain fell a sleep while waiting for something new).

von Ryan M

25. Aug. 2020

While I feel like I have a good theoretical understanding of the issues involved in classification, with an understanding of how the algorithms work and how to implement them, this course could have prepared me better to attack an actual problem by following a real case study through, showing me what steps someone with experience in attacking real problems would take in order to come up with a good classifier.

In particular, while a number of classifiers were presented, there was little to no discussion of the relative advantages and disadvantages of each algorithm. In what cases should I choose logistic regression? A decision tree or a boosted decision tree?

Finally, it seems that random forests and support vector machines are common classifiers, and this course did not cover them. I instead had to learn about random forests (a relatively simple concept that could have been included with the boosted decision tree content) from scikit-learn's web site.