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

4.7
2,875 Bewertungen
479 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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26 - 50 von 447 Bewertungen für Machine Learning: Classification

von Chandan D

Aug 25, 2018

I really enjoyed learning this course on Machine Learning Classification!

von Arun K P

Oct 17, 2018

G

von Fakrudeen A A

Sep 15, 2018

Excellent course - teaches linear, logistic regression and decision trees. It also teaches the most important concept of precision-recall. Overall highly recommended.

von Maxwell N M

Oct 07, 2018

Great Course!

Teachers are genius and awesome

Thanks

von Courage S

Oct 22, 2018

Excellent Teaching with meticulous details and great humor. BIG Plus.

von Illia K

Oct 25, 2018

Very useful!

von Thomas K

Oct 29, 2018

In my opinion, so far the best part in the specialization series. The only thing, that was strange for me is that the effort required for programming varied a lot. So from week to week, it was difficult to predict how much time and effort would be needed to finish the assignments in time.

von SANKARBALAJI M

Oct 30, 2018

Good learning

von Aayush A

Jul 16, 2018

very good course for classification.

von Pankaj K

Sep 25, 2017

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

von D D

Oct 16, 2016

Nice videos. Learned a lot. Also videos good for future review.

von Vladimir V

Jun 14, 2017

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

von Sandeep K S

May 07, 2016

awesome course awesome teachers

von Henry H

Nov 18, 2016

Very clear and easy to understand.

von clark.bourne

May 09, 2016

Professional, comprehensive, worth to learn

von B M K

Oct 16, 2016

Challenging and Exciting Course. Lots of ML concepts (Decision Trees, AdaBoost, Ensembles, Stochastic gradient, loglikelyhood etc. ) are introduced and i believe this course is of extreme importance in laying the fundamentals of ML.

von Muhammad H S

Nov 02, 2016

Excellent

von Sergio D H

Jul 22, 2016

AWESOME COURSE!! Carlos and Emily are incredible teachers and the course contents are truly informative and well-paced for beginners.

von Norberto S

Oct 09, 2016

Excellent course with lots of practical exercises.

von 嵇昊雨

Apr 26, 2017

Great materials for learning Classification

von Filipe P L

Oct 03, 2016

Very good, sometimes is a little hard, but is very helpful and have a lot of practical exercises

von Jifu Z

Jul 23, 2016

Good class, But it would be much better if the quiz is open to those who doesn't pay.

von Kumiko K

Jun 05, 2016

Fun!

von Shanchuan L

Dec 07, 2016

This is a perfect course

von LIU Y

Mar 22, 2016

best of the best, theoretically and practically