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Вернуться к Machine Learning: Classification

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

Оценки: 3,670

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


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 :)


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:

201–225 из 575 отзывов о курсе Machine Learning: Classification

автор: Thierry Y

12 нояб. 2017 г.

Great material, easy to follow, and nice examples around sushis :)

автор: Christian R

11 сент. 2017 г.

The visualizations provide deeper understanding in the algorithms.

автор: Luis M

28 янв. 2017 г.

Lots of practical tips, some applicabe not only to Classification.

автор: Yoshifumi S

8 мая 2016 г.

As always in this specialization, tough course but so practical !!

автор: Japneet S C

5 февр. 2018 г.

Course is very good. Concepts are explained in a very simple way.

автор: dragonet

24 мар. 2016 г.

thank you every much, every helpful! ~i will repeat several time~

автор: Mark W

6 мая 2017 г.

Fantastic Lecturers and very interesting and informative course

автор: D D

16 окт. 2016 г.

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

автор: Eric N

11 окт. 2020 г.

Excellent online teaching with clear and concise explanations!

автор: Parab N S

12 окт. 2019 г.

Excellent course on Classification by University of Washington

автор: Mohd A

14 авг. 2016 г.

Learning is fun when you have professors like Carlos Guestrin.

автор: Ali A

4 сент. 2017 г.

the course material is great but the assignments are not good

автор: clara c

11 июня 2016 г.

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

автор: Yufeng X

14 июня 2019 г.

The lecture is super. The exams could be more challenging-:)

автор: Sarah W

24 сент. 2017 г.

Great course! Learned so much! So excited to use this stuff!

автор: Tony T

19 нояб. 2016 г.

funny and enthusiastic lecturer make a dry subject more fun.

автор: Simbarashe M

24 сент. 2020 г.

l know a knew way to train the models taught in this course

автор: Isaac B

20 нояб. 2016 г.

Excellent course. Practical understanding of classification

автор: Ali A

21 мар. 2016 г.

So far it is a mazing. I will rate at the end of the course

автор: Kartik W

19 сент. 2020 г.

A must do course for all the machine learning enthusiasts.

автор: Koen O

14 апр. 2017 г.

Excellent course for learning the basics on classification

автор: Chao L

31 мар. 2017 г.

Nicely formatted. And it's quite intuitive and practical.

автор: Patrick P

28 нояб. 2016 г.

Very good and and informative to start with this subject.

автор: vacous

3 авг. 2017 г.

very nice material covering the basic of classification.

автор: Xuan Q

13 февр. 2017 г.

Super useful and a bit of challenging! Really enjoy it.