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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: 3,611
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:

176–200 из 566 отзывов о курсе Machine Learning: Classification

автор: Michael O T

29 нояб. 2019 г.

A great professor and a lot of knowledge about machine learning classification

автор: Suresh K P

19 дек. 2017 г.

This course much helpful and understandable easily compared previous sessions.

автор: Daopeng S

12 апр. 2016 г.

A very good introduce machine learning course, it's clear and easy to follow.

автор: Daniel Z

8 мар. 2016 г.

This is a hand-on very exciting course, strongly recommended for all audience

автор: Xavi R

19 янв. 2021 г.

This is a great course! The professors are great and the material is clear!

автор: Vladimir V

14 июня 2017 г.

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

автор: James M

20 июля 2016 г.

Top notch. Great course design. Best value for money in Machine Learning!

автор: Javier A

25 нояб. 2018 г.

Quite Interesting. Entertaining and the lectures are quite easy to follow.

автор: Kazi N H

23 июня 2016 г.

One of the awesome course on classification. Just so perfect for learning.

автор: Chandan D

25 авг. 2018 г.

I really enjoyed learning this course on Machine Learning Classification!

автор: Zuozhi W

8 февр. 2017 г.

Very informative class! The lectures are slow, clear, and easy to follow.

автор: Pankaj K

25 сент. 2017 г.

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

автор: Zhongkai M

12 февр. 2019 г.

Great course, provided details that not show in others' and textbooks.

автор: courage s

22 окт. 2018 г.

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

автор: Jean-Etienne K

24 июля 2016 г.

intuitive, clear and practical. The best explanation I found so far !

автор: akashkr1498

19 мая 2019 г.

good course but make quize and assignment quize more understandable

автор: Alexandre N

20 дек. 2016 г.

Excellent course with plenty of intuition and practical experiments.

автор: eric g

21 мар. 2016 г.

The best part for me in this specialization, Classification is great

автор: Swapnil A

6 сент. 2020 г.

Really awesome course. Dr. Carlos explains everything from scratch.

автор: Karthik M

1 июня 2019 г.

Excellent course and the instructors cover all the important topics

автор: Srinivas J

12 нояб. 2016 г.

truly enjoyed this course and recommended to my colleagues as well.

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 !!