Chevron Left
Zurück zu Machine Learning: Classification

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

4.7
Sterne
3,610 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)....

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:

126 - 150 von 566 Bewertungen für Machine Learning: Classification

von Ornella G

1. Okt. 2016

I really enjoyed the topics presented and the fluid way to present them. It's a very well done summary of the classification models.

von Siddharth S

8. Jan. 2018

Excellent course and all the concepts have been explained very simply and with an element of fun.

Many thanks to Emily and Carlos...

von Alvin B K

28. Sep. 2020

This was a very great course. I got the confidence to use ML algorithms and concepts efficiently and also write my own algorithms.

von Gaurav c

22. Mai 2019

Would have loved even more had Carlos explained his students gradient boosting as well. I liked the way of his taught in lectures.

von Ankur P

29. Mai 2018

Loved the way our tutor (Carlos) explained the concepts to us. Things are getting clearer with each course in ML :) Many thanks :)

von Renato R S

27. Aug. 2016

All the basics - and much of the advanced stuff - is presented, in a coherent and inspired way. Thanks for crafting such a course.

von Joseph F

5. Apr. 2018

Good course with many assignment to design the algorithm with your own code. But I think this course last a little bit too long.

von Tanachote R

25. Apr. 2020

Thank you for sharing your knowledge to me. This course is very good and I really appreciate both of you (Carlos and Emily)

von Reinhold L

21. März 2019

Very good course for classification in machine learning - top presentation documents - very well structured and practical

von Pawan K S

15. Mai 2016

Nice course with appropriate amount of detail in it! Covers tough mathematical aspect for those who are interested in it.

von Fabio P

18. Apr. 2016

Very interesting topic with some advanced topics covered. It really shows how to use machine learning in the real world.

von Matthew S

22. Mai 2020

Great! Not horribly wretchedly awful, but actually very good! (With this class I hope this is classified correctly!)

von Vibhutesh K S

22. Mai 2019

It was a very detailed course. I wished, doing it much earlier in my research career. Great insights and Exercises.

von Igor K

16. März 2016

very interesting and novice friendly, however some math (basic matrix calculus and derivatives) review worth doing

von Etienne V

13. Nov. 2016

Great course with very good material! I'd like to see assignments that leaves more coding tasks to the student.

von Naman M

9. Juli 2019

you can't find a better course on machine learning as compared to this one. Simply the best course on coursera

von Divyang S

13. Sep. 2020

Excellent and very in-depth coverage of basic and advanced concepts... Perhaps the best course out there !!!!

von Emil K

29. Jan. 2020

Such a great course. Brings the math behind machine learning to users without a math background. Thank you.

von Naimisha S

30. Juli 2018

Availability of the Ipython notebook makes it easy to solve the Quizzes which has step by step explaination

von Konstantinos P

28. März 2017

The context and the structure of the course is absolutely perfect. Also, Carlos is the perfect professor!

von Hristo V

1. Dez. 2016

The course is absolutely amazing! Very clear explanation of the concepts with great notebook assignments.

von Shaowei P

31. März 2016

great course, would have been even more great if there are more details on how to use boosting for kaggle

von Rashi K

17. März 2016

Assignments were more challenging than previous course. Loved solving them. Enjoyed the optional videos.

von Dmitri T

25. Apr. 2016

Really liked the practical application of this course - very useful in learning classification methods.

von Deepak S

21. Aug. 2020

Assignments are great providing an opportunity to have better understanding about the topic discussed