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Вернуться к Machine Learning: Classification

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

Оценки: 3,668
Рецензии: 605

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:

451–475 из 574 отзывов о курсе Machine Learning: Classification

автор: Dawid L

20 мар. 2017 г.

Presented content is rather clear and instructors are rather easy to follow. Only the assignments are often confusing as there are questions which refer to missing content.

автор: Thuc D X

27 июня 2019 г.

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

автор: Gaurav K J

1 мая 2018 г.

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.

автор: Justin K

10 июня 2016 г.

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

автор: Hao H

12 июня 2016 г.

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.

автор: 김대성

23 мар. 2021 г.

Very nice lecture & materials. The only slight negative component this lecture contains is the library used for the programming assignment.

автор: Fangzhe G

7 февр. 2020 г.

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

автор: Brian B

22 апр. 2016 г.

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

автор: Fahad S

3 нояб. 2018 г.

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

автор: Aaron

3 июля 2020 г.

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

автор: Alexis C

29 сент. 2016 г.

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

автор: Kishaan J

1 июля 2017 г.

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

автор: Raisa M

19 авг. 2017 г.

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

автор: Ning A

16 сент. 2016 г.

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

автор: Yingnan X

14 апр. 2016 г.

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

автор: Oleg R

9 окт. 2016 г.

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

автор: Thrivikrama

12 окт. 2016 г.

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

автор: Tomasz J

4 апр. 2016 г.

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

автор: Baubak G

10 июня 2018 г.

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

автор: Simon C

1 мая 2020 г.

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

автор: Scott A

19 июля 2021 г.

Class was inconsistent, it started very detailed and became over-simplified in the later weeks.

автор: Srinivas C

2 дек. 2018 г.

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

автор: Sapna A

2 февр. 2021 г.

The course was awesome, especially with sentimental classification case explanation... Thanks

автор: ZhangBoyu

20 июля 2018 г.

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

автор: shashank a

9 июня 2020 г.

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