Chevron Left
Machine Learning: Classification(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Classification 학습자 리뷰 및 피드백

3,641개의 평가
601개의 리뷰

강좌 소개

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)....

최상위 리뷰

2020년 6월 14일

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 :)

2016년 10월 15일

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!

필터링 기준:

Machine Learning: Classification의 570개 리뷰 중 176~200

교육 기관: Shawon P

2021년 7월 22일

A must take course for every individual trying to understand Machine Learning.

교육 기관: Michael O T

2019년 11월 29일

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

교육 기관: Suresh K P

2017년 12월 19일

This course much helpful and understandable easily compared previous sessions.

교육 기관: Daopeng S

2016년 4월 12일

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

교육 기관: Daniel Z

2016년 3월 8일

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

교육 기관: Xavi R

2021년 1월 19일

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

교육 기관: Vladimir V

2017년 6월 14일

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

교육 기관: James M

2016년 7월 20일

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

교육 기관: Javier A

2018년 11월 25일

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

교육 기관: Kazi N H

2016년 6월 23일

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

교육 기관: Chandan D

2018년 8월 25일

I really enjoyed learning this course on Machine Learning Classification!

교육 기관: Zuozhi W

2017년 2월 8일

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

교육 기관: Pankaj K

2017년 9월 25일

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

교육 기관: Zhongkai M

2019년 2월 12일

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

교육 기관: courage s

2018년 10월 22일

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

교육 기관: Jean-Etienne K

2016년 7월 24일

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

교육 기관: akashkr1498

2019년 5월 19일

good course but make quize and assignment quize more understandable

교육 기관: Alexandre N

2016년 12월 20일

Excellent course with plenty of intuition and practical experiments.

교육 기관: eric g

2016년 3월 21일

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

교육 기관: Swapnil A

2020년 9월 6일

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

교육 기관: Karthik M

2019년 6월 1일

Excellent course and the instructors cover all the important topics

교육 기관: Srinivas J

2016년 11월 12일

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

교육 기관: Thierry Y

2017년 11월 12일

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

교육 기관: Christian R

2017년 9월 11일

The visualizations provide deeper understanding in the algorithms.

교육 기관: Luis M

2017년 1월 28일

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