Chevron Left
Zurück zu Machine Learning: Classification

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

4.7
Sterne
3,682 Bewertungen

Ãœber 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)....

Top-Bewertungen

SM

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 :)

SS

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!

Filtern nach:

301 - 325 von 578 Bewertungen für Machine Learning: Classification

von Lixin L

•

7. Mai 2017

von MRS. G

•

9. Mai 2020

von Satish K D

•

3. Feb. 2019

von FanPingjie

•

9. Dez. 2018

von Lars N

•

4. Okt. 2016

von Venkata D

•

14. Apr. 2016

von Brian N

•

20. Mai 2018

von Mark h

•

27. Juli 2017

von Shiva R

•

16. Apr. 2017

von Shanchuan L

•

7. Dez. 2016

von Changik C

•

25. Okt. 2016

von Alexander S

•

7. Aug. 2016

von Yacine M T

•

31. Juli 2019

von Fakhre A

•

17. Feb. 2017

von Weituo H

•

14. März 2016

von Gaurav K

•

19. Sep. 2020

von GURUSUBRAMANI. S

•

24. Mai 2020

von Kevin Y

•

26. Juni 2017

von Sami A

•

20. Mai 2016

von stephon_lu

•

23. Dez. 2017

von Michael P

•

6. Dez. 2016

von 쥬

•

30. Juni 2016

von AJAY K

•

13. Okt. 2019

von Muhammad Z H

•

30. Aug. 2019

von Luis E T N

•

4. Juli 2017

Coursera Footer

Starten oder fördern Sie Ihre Karriere

Beliebte Themen durchsuchen

Beliebte Kurse und Artikel

Abschluss oder Zertifikat online erwerben

Coursera

Community

Mehr

Ãœberall lernen
Placeholder
Placeholder
Placeholder