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

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572 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

Jun 15, 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

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!

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426 - 450 von 542 Bewertungen für Machine Learning: Classification

von Thuc D X

Jun 28, 2019

Sometimes the assignment description was hard to follow along. Overall, the course equips me a good understand and practical skills to tackle classification tasks.

von Gaurav K J

May 01, 2018

I learnt a lot, but I feel course 2 was very well made and this one felt a bit unstructured in comparison. Also, assignments in this course were made very easy.

von Justin K

Jun 10, 2016

Assignments were a little too easy, considering that students are expected to have taken the first two courses in the specialization. Otherwise, great course!

von Hao H

Jun 12, 2016

Good course overall. Some difficult materials such as boosting were not clear enough and I had to look into a few online resources to really understand it.

von Fangzhe G

Feb 07, 2020

This course could be better if more programming content was taught. The programming assignments are difficult and not taught in courses.

von Brian B

Apr 22, 2016

Great course. I'm really looking forward to learn more about clustering in the next course since I know nearly nothing about clustering.

von Fahad S

Nov 03, 2018

The content was excellent and the exercises were really good. It would be better if svms and bayesian classifiers are also covered

von Aaron

Jul 04, 2020

Nice course for new learner of machine learning, but I do hope this course could have introduction to support vector machine.

von Alexis C

Sep 29, 2016

wanted more sophisticated mathematics and intuition (as opposed to simpler explanations). [regression course had this ...]

von Kishaan J

Jul 01, 2017

Really loved this course! The insights into decision trees and precision-recall couldn't have been any better! Thank you!

von Raisa

Aug 19, 2017

Wanted some stuff on SVM and Dimensionality Reduction. Awaiting for a course on Recommender Systems and Deep Learning

von Ning A

Sep 16, 2016

Learn more than just classification, but also learn how to understand the ideas behind classification algorithms.

von Yingnan X

Apr 14, 2016

A good course to start learning classifications and getting exposure to algorithms. The instructor is awesome!!

von Oleg R

Oct 09, 2016

I would prefer more complex assignments and more advanced math concepts in the course. Otherwise it is great.

von Thrivikrama

Oct 12, 2016

Good course.. Should have SVM related info too -- waiting for the promised optional videos from Prof. Carlos

von Tomasz J

Apr 04, 2016

Great course! However I put only 4 starts because I would like to see random forests which are not present.

von Baubak G

Jun 10, 2018

I think the course on boosting could be worked on better. But all in all I really enjoyed this course.

von Simon C

May 01, 2020

It's still a great course. But I think the quality of the regression one is better than this overall.

von Srinivas C

Dec 02, 2018

This course was really good and helped in understanding different techniques in Classification

von ZhangBoyu

Jul 20, 2018

The lecturer speaks in a quite unclear manner, besides, everything is great and detailed.

von Shashank A

Jun 09, 2020

Overall good, But it seems like same type of questions are repeated in assignment quiz

von Rattaphon H

Aug 13, 2016

The questions are hard to understand and ambiguous though their answers are easy.

von Bruno G E

Apr 17, 2016

Lack some of classical classification algorithms like SVM and Neural Netwroks.

von Jacob M L

Jun 24, 2016

Very approachable material, given the diversity of classification algorithms.

von hiram y s

Apr 26, 2020

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