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

3,670 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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351 - 375 von 575 Bewertungen für Machine Learning: Classification

von Jesús U S

26. Juni 2020

Awesome Course!

von Jerome Z

4. Juli 2018

Very good class

von Paulo R M B

30. Jan. 2017

Well explaned !

von Pandu R

20. Apr. 2016

Worth the wait.

von Roberto C

18. Mai 2020

Simply amazing


4. Mai 2020

Very well done

von Manan M

20. Apr. 2020

Amazing course

von avishek k

19. Juli 2020

great content

von Gaurav G

26. Dez. 2018

Good Course!!


30. Okt. 2018

Good learning

von Yang X

29. Okt. 2017

Very helpful!

von Omar B

9. Feb. 2017

Great course.

von Zizhen W

3. Nov. 2016

Pretty Solid!

von Manuel S

11. Sep. 2016

Great course!

von Salim T T

13. Apr. 2021

Good course!

von Francisco R M

21. Feb. 2021

Great course

von Sumit K J

24. Jan. 2021

Great Course

von Aaqib M

20. Sep. 2020

Great course

von Manikant R

19. Juni 2020

Great course

von Dr. R R N

11. Mai 2020

Nice course.

von Hanna L

12. Aug. 2019

Great class!

von Illia K

25. Okt. 2018

Very useful!

von Shuang D

28. Juni 2018

nice course!

von Krishna C

16. Sep. 2017

Great Course

von Krzysztof S

6. Juni 2017

great course