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Machine Learning: Classification(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Classification 학습자 리뷰 및 피드백

3,670개의 평가

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 575개 리뷰 중 151~175

교육 기관: Rashi K

2016년 3월 17일

Assignments were more challenging than previous course. Loved solving them. Enjoyed the optional videos.

교육 기관: Dmitri T

2016년 4월 25일

Really liked the practical application of this course - very useful in learning classification methods.

교육 기관: Deepak S

2020년 8월 21일

Assignments are great providing an opportunity to have better understanding about the topic discussed


2019년 5월 3일

This course will provide you clear and detailed explanation of all the topics of Classification.

교육 기관: Jonathan C

2018년 1월 19일

wow this was a good course. things got real here and hard. but I feel like I can do anything now

교육 기관: Yuexiu C

2017년 1월 20일

The instructor is awesome. He explained the boring statistical method in a very interesting way!

교육 기관: Filipe P L

2016년 10월 2일

Very good, sometimes is a little hard, but is very helpful and have a lot of practical exercises

교육 기관: Evgeni S

2016년 6월 10일

Very focused overview of different classification methods. Goes deeper than in other ML classes.

교육 기관: Patrick M

2016년 8월 8일

Excellent course. Great mix of theory overview coupled with practical examples to work through.

교육 기관: Ayush K G

2017년 11월 1일

Usefull for getting ideas and depth knowledge in Classification. Explained in very simple way.

교육 기관: Arslan a

2019년 2월 18일

the person who wants to start career in machine learning must take this course! Its awsome :)

교육 기관: Evaldas B

2017년 12월 14일

Very nice course with a little bit of details about how classification is done. Enjoyed it.

교육 기관: Aakash S

2019년 6월 14일

Amazing Explanation of every thing related to Classification.

Thanks a lot for the course.

교육 기관: Viktor K

2021년 5월 14일

I m learn many things in the coursera. This is one of the best app provide for everyone.

교육 기관: Gustavo d A C

2017년 4월 23일

It was a nice course. I could learn many new techniques and algorithms. Very exciting !!

교육 기관: Mounika G

2020년 5월 3일

I have learnt many things from these course .This course helped me to learn from online

교육 기관: Rahul M

2017년 11월 12일

awesome course material to nourish your brain to classify in better decision making...

교육 기관: Kim K L

2016년 8월 13일

Another classic and fantastic. Love this Course and learn so much. Highly recommended!

교육 기관: Patrick A

2020년 6월 27일

As usual, very simple way of explaining principles. Thanks very much for this course!

교육 기관: andreas c c

2017년 8월 16일

The course is demanding but I learn a lot in classification.

The teachers are awesome!

교육 기관: Simon C

2016년 10월 28일

Great content and exercises which facilitated understanding of very complex concepts.

교육 기관: Jifu Z

2016년 7월 22일

Good class, But it would be much better if the quiz is open to those who doesn't pay.

교육 기관: Sanjay M

2017년 6월 30일

Very nice course with good mix of machine learning concepts with maths, programming.

교육 기관: Suraj P

2021년 7월 19일

Nice Course for detail understanding of machine learning classification algorithms.

교육 기관: Saheed S

2017년 7월 18일

It was a great course, I will start working on a new classification project. Thanks