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Bewertung und Feedback des Lernenden für Machine Learning: Classification von University of Washington

3,611 Bewertungen
597 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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251 - 275 von 566 Bewertungen für Machine Learning: Classification

von Abhishek G

22. Juni 2016

The quizzes can be a bit more challenging


18. Juli 2018

Very clear and useful course, excellent.

von Hansel G M

1. Nov. 2017

Great course !!! I totally recommend it.

von Aditi R

20. Okt. 2016

Wonderful experience. Prof is very good.

von Madhusudhan r D

27. Juni 2020

Ex ordinary subject with nice concepts.

von Israel C

30. Mai 2017

One of the best courses i've ever tried

von Garvish

14. Juni 2017

Great Information and organised course

von Lei Q

16. März 2016

Excellent theory and practice(coding)!

von David P

27. Juni 2020

A great course and a great teacher!!!

von MAO M

6. Mai 2019

lots of work. very good for beginners

von Dhruvil S

10. Jan. 2018

Nice Course Clears a lot of concepts.

von Xue

14. Dez. 2018

Very good lessons on classification.

von Aayush A

16. Juli 2018

very good course for classification.

von Colin B

9. Apr. 2017

Really interesting course, as usual.

von Jialie ( Y

8. Feb. 2019

It is really useful and up to date.

von Sean L

31. Aug. 2016

wonderful course for beginner of ML

von Cosmos D I

29. März 2020

This course is very informational!

von Alessandro B

31. Okt. 2017

nice, clear engaging ...and useful

von 易灿

28. Nov. 2016


von Henry H

17. Nov. 2016

Very clear and easy to understand.

von Albert V d M

8. März 2016

Very instructive, you learn a lot.

von Angel S

8. März 2016

Awesome. Waiting for the next one.

von Jing

14. Aug. 2017

Better than the regression course

von Rishabh J

19. Dez. 2016

Amazing course, Amazing teaching.


29. Mai 2020