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Вернуться к Machine Learning: Classification

Отзывы учащихся о курсе Machine Learning: Classification от партнера Вашингтонский университет

4.7
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Оценки: 3,613
Рецензии: 597

О курсе

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)....

Лучшие рецензии

SM
14 июня 2020 г.

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 окт. 2016 г.

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!

Фильтр по:

251–275 из 566 отзывов о курсе Machine Learning: Classification

автор: Abhishek G

22 июня 2016 г.

The quizzes can be a bit more challenging

автор: VITTE

18 июля 2018 г.

Very clear and useful course, excellent.

автор: Hansel G M

1 нояб. 2017 г.

Great course !!! I totally recommend it.

автор: Aditi R

20 окт. 2016 г.

Wonderful experience. Prof is very good.

автор: Madhusudhan r D

27 июня 2020 г.

Ex ordinary subject with nice concepts.

автор: Israel C

30 мая 2017 г.

One of the best courses i've ever tried

автор: Garvish

14 июня 2017 г.

Great Information and organised course

автор: Lei Q

16 мар. 2016 г.

Excellent theory and practice(coding)!

автор: David P

27 июня 2020 г.

A great course and a great teacher!!!

автор: MAO M

6 мая 2019 г.

lots of work. very good for beginners

автор: Dhruvil S

10 янв. 2018 г.

Nice Course Clears a lot of concepts.

автор: Xue

14 дек. 2018 г.

Very good lessons on classification.

автор: Aayush A

16 июля 2018 г.

very good course for classification.

автор: Colin B

9 апр. 2017 г.

Really interesting course, as usual.

автор: Jialie ( Y

8 февр. 2019 г.

It is really useful and up to date.

автор: Sean L

31 авг. 2016 г.

wonderful course for beginner of ML

автор: Cosmos D I

29 мар. 2020 г.

This course is very informational!

автор: Alessandro B

31 окт. 2017 г.

nice, clear engaging ...and useful

автор: 易灿

28 нояб. 2016 г.

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!

автор: Henry H

17 нояб. 2016 г.

Very clear and easy to understand.

автор: Albert V d M

8 мар. 2016 г.

Very instructive, you learn a lot.

автор: Angel S

8 мар. 2016 г.

Awesome. Waiting for the next one.

автор: Jing

14 авг. 2017 г.

Better than the regression course

автор: Rishabh J

19 дек. 2016 г.

Amazing course, Amazing teaching.

автор: CHERUKURI S V N K

29 мая 2020 г.

IT WAS EXCELLENT AND ENJOYED IT.