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Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Classification von University of Washington

4.7
2,896 Bewertungen
481 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

SS

Oct 16, 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!

CJ

Jan 25, 2017

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses

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176 - 200 von 449 Bewertungen für Machine Learning: Classification

von Omar B

Feb 09, 2017

Great course.

von clara c

Jun 11, 2016

This course was great! I really enjoyed it and learned a lot.

von Edward F

Jun 25, 2017

I took the 4 (formerly 6) courses that comprised this certification, so I'm going to provide the same review for all of them.

This course and the specialization are fantastic. The subject matter is very interesting, at least to me, and the professors are excellent, conveying what could be considered advanced material in a very down-to-Earth way. The tools they provide to examine the material are useful and they stretch you out just far enough.

My only regret/negative is that they were unable to complete the full syllabus promised for this specialization, which included recommender systems and deep learning. I hope they get to do that some day.

von Michael P

Dec 06, 2016

Awesome, not awful;)

von Sean L

Aug 31, 2016

wonderful course for beginner of ML

von Ridhwanul H

Oct 16, 2017

As usual this was also a great course, except

⊃゜Д゜)⊃ decision trees ⊂(゜Д゜⊂

I am not saying presently anythings bad or incorrect, but I just dont feel familiar with this. It is one tough topic to understand. I think it would have been great if there were some videos and lectures where some programming example were also given, this would have helped out a lot in programming assignments.

Also there is another thing that I think should have been addressed (at least in one of the courses, unless you did it in course 4 the last one which I havent done yet) : vectorisation - instead of looping through each weight how it could be achieved at once through vectorisation.

von Roger S

Sep 04, 2016

This course is COOL

von Mike M

Jul 16, 2016

Learned a lot, great course!

von Daopeng S

Apr 12, 2016

A very good introduce machine learning course, it's clear and easy to follow.

von Albert V d M

Mar 08, 2016

Very instructive, you learn a lot.

von Marios A

Mar 08, 2016

The course is really well structured and gives a solid understanding in the latest approaches in Machine Learning. However I would also like to see in this course more sophisticated math, because it matters and I think there are important.

von Akshay B

May 24, 2017

Excellent and intuitive introduction to classification.Certainly a lighthouse in a rather overwhelming and chaotic learning scenario of machine learning we have now a days(Highly recommended for both mathematics and programming student)

von 易灿

Nov 28, 2016

课程很生动,讲的很详细,真心谢谢导师!希望能在算法后面多提供点资料!

von David E

Aug 21, 2016

very useful course : covers a range of very practical and useful topics I had heard about but didn't fully understand until taking this course. Some highlights stochastic gradient, boosting, and precision-recall trade offs.

von M L

Mar 14, 2016

Great course!

Personally I could use a little more on the math behind the algorithms (e.g. Adaboost, why does it work?).

Also, would be great to add SVM in next iterations of this class.

Thanks!

von Angel S

Mar 08, 2016

Awesome. Waiting for the next one.

von Zizhen W

Nov 03, 2016

Pretty Solid!

von Paul C

Aug 13, 2016

This Machine Learning class and the rest of the Machine Learning series from the University of Washington is the best material on the subject matter. What really sets this course and series apart is the case-base methodology as well as in-depth technical subject matter. Specifically, the step through coding of the algorithms provides key insight that is seriously missed in other classes even in traditional academic settings. I highly encourage the authors and other Coursera publishers to continue to publish more educational material in the same framework.

von Arash A

Dec 01, 2016

Learned a lot and enjoyed even more. Thanks!

von Pankaj K

Sep 25, 2017

Great challenging and deep assignments! Big Thanks to both professors!!

von Nitish V

Jul 06, 2017

The course is well designed for both beginners and experts . The concepts are well explained and the assignments are really challenging. Best thing is , it talks more from practical aspects . The optional sections are really good.

von Alexander S

Aug 07, 2016

one of the best courses.

von Vladimir V

Jun 14, 2017

Awesome course! Highly recommend for anyone interested in machine learning.

von Shiva R

Apr 16, 2017

Exceptional and Intutive

von André F d A F C

Jul 25, 2016

Excellent course.