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Machine Learning: Classification に戻る

ワシントン大学(University of Washington) による Machine Learning: Classification の受講者のレビューおよびフィードバック

4.7
2,988件の評価
492件のレビュー

コースについて

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)....

人気のレビュー

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

フィルター:

Machine Learning: Classification: 101 - 125 / 461 レビュー

by Richard L

Oct 15, 2016

Great course. The lectures and programming assignments have been extremely beneficial to help me get a basic foundation of ML classification.

by Fan D

Feb 02, 2017

This course is alright. For some reason I liked the regression course more as this one was a little to simple in terms of the practical.

by venkatpullela

Nov 17, 2016

Course is really good. Assignments are taking too much time if you want to do the course rally fast, with questionable learning value.

by 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.

by Nitin D

Dec 18, 2018

Excellent lessons on this important topic Classification. I think all major areas were explained quite nicely, with proper examples.

by Dongliang Z

Mar 22, 2018

Excellent course! The teacher explained a lot of intuitions during the course. The optional part s are very interesting and helpful.

by Ornella G

Oct 01, 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.

by Siddharth S

Jan 09, 2018

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

Many thanks to Emily and Carlos...

by Gaurav C

May 22, 2019

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

by Ankur P

May 29, 2018

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

by Renato R S

Aug 27, 2016

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

by Joseph F

Apr 05, 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.

by Reinhold L

Mar 21, 2019

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

by Pawan K S

May 15, 2016

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

by Fabio P

Apr 18, 2016

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

by Vibhutesh K S

May 22, 2019

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

by Igor K

Mar 16, 2016

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

by Etienne V

Nov 13, 2016

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

by Naman M

Jul 09, 2019

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

by Emil K

Jan 29, 2020

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

by Naimisha S

Jul 30, 2018

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

by Konstantinos P

Mar 28, 2017

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

by Hristo V

Dec 01, 2016

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

by Shaowei P

Mar 31, 2016

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

by Rashi K

Mar 17, 2016

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