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

3,659 Bewertungen
603 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 572 Bewertungen für Machine Learning: Classification

von Jacob M L

24. Juni 2016

Very approachable material, given the diversity of classification algorithms.

von hiram y s

26. Apr. 2020

Very well explained and with careful guidance through the programming steps.

von Luiz C

7. Juni 2018

Clear, good engaging videos, good quality/complexity balance of exercises

von Zebin W

24. Aug. 2016

It covers many aspects in clustering and the assignments are very helpful

von Luis d l O

22. Juni 2016

Very easy to follow and didactic. Very good material in the assignments.

von Sander v d O

9. Mai 2016

Simply a great course. Good intro to machine learning classifiation.

von Franklin W

4. Mai 2017

Great beginner/advanced course for Machine Learning Classification!

von Pascal U E

7. März 2016

Take you too long to come back, but the content is great. Good job

von Michael B

4. Sep. 2016

Good survey of the material, but assignments are superficial.

von vardan l

26. Jan. 2018

Some instructions in programming assignments are not clear.

von Charan S

30. Juli 2017

Very nice course, detailed explanations and visualizations.

von Sahil M

10. Juli 2018

Was a good course with some in-depth topics covered!

von Jiancheng

20. März 2016

good course but too much easy, can be a good review.

von Hanqiao L

9. Aug. 2016

Need more content for SVM and Random Forest

von Alejandro T

9. Sep. 2017

It's a really good course, really liked it

von Mohit G

2. Feb. 2019

Good, insightful but repetitive coding.

von Sah-moo K

3. Apr. 2016

Decision trees and boosting were great.

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 Shashwat 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.