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Machine Learning: Classification(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Classification 학습자 리뷰 및 피드백

4.7
별점
3,637개의 평가
599개의 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

SM
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 :)

SS
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 101~125

교육 기관: Leonardo T L

2020년 10월 7일

Great course about general classification approaches and techniques. The pace of the classes is great. One more time the professors surpassed my expectations!

교육 기관: Bharat J

2018년 1월 19일

I wish we had 5th course too,All courses are well organized and can be completed with other tool.

Hope they also include SVM and start courses on deep learning

교육 기관: Ganesan P

2017년 2월 6일

A very good course - understood a lot about classification and the understanding gained will help in reading text books like Ian Good Fellow for deep learning

교육 기관: Alex L

2016년 3월 7일

Great courses as usual like the previous courses in this specialization. Cater for beginners who want to gain a strong foundation and practical usages for ML.

교육 기관: Babak P

2018년 6월 28일

Great exposure that requires hand coding the algorithms. Really makes the concepts stick with a perfect combination of theory and programming mixed together.

교육 기관: Farrukh N A

2017년 2월 10일

I found carols to be the best instructor in machine learning domain, he presented the algorithms and all core machine learning concepts in really great way.

교육 기관: OG

2016년 8월 3일

A great combination between down to earth concepts and their implementations in python. Implementation of topics in plain python is what I enjoyed the most.

교육 기관: Jane z

2020년 1월 26일

The hands-on approach is excellent. Not only I learned ML / Classification, I was able to practice Python skills and statistical skills as well.

THANK YOU!

교육 기관: Nikolay C

2016년 3월 16일

Excellent course! I've learned these topics before, but many things were not clear enough. While learning this course my knowledge really improved a lot.

교육 기관: Usman

2016년 11월 13일

I think support vector machines is an important topic which is missing. Anyway, the programming assignments were terrific. I really enjoyed this course!

교육 기관: Andrea C

2016년 9월 7일

The course covers most important topics in depth and exercises are very interesting, them helps you to reason about some important theoretical concepts.

교육 기관: Youssef R

2017년 8월 23일

This is really a wonderfull course, and i recommend it to anyone who want to master some important techniques in the trending field of machine learning

교육 기관: Josef H

2016년 11월 26일

I like the detailed comparison between choosing different parameters for creating the classification model. I learn a lot of tricks for creating plots.

교육 기관: Suoyuan S

2016년 4월 21일

This course is friendly to machine learning beginners for the learning material is easy to understand as well as the assignment is easy to accomplish.

교육 기관: Sara E E

2018년 3월 29일

It is very intuitive and easy to follow.

I hope you add SVM and talk about linear/nonlinear decision boundaries in the next enhancement to the course.

교육 기관: m w

2017년 12월 23일

While I enjoyed most of the exercises, I found some of the implementations to be more puzzle solving rather than deeply understanding the algorithms.

교육 기관: Gunjari B

2018년 5월 21일

An absolute marvel of a course! In depth explanation to everything, detailed and important concepts explained so much at ease with Carlos' humour!

교육 기관: RAMESH K M

2016년 8월 1일

The course has be described in a very precise manner. The instructor takes time to clearly explain the concepts and the importance of the same.

교육 기관: Filipe G

2016년 4월 2일

The best machine learning course I took online. I've taken other coursera courses, and this is the most complete, comprehensive, and well made.

교육 기관: Richard L

2016년 10월 15일

Great course. The lectures and programming assignments have been extremely beneficial to help me get a basic foundation of ML classification.

교육 기관: Fan D

2017년 2월 2일

This course is alright. For some reason I liked the regression course more as this one was a little to simple in terms of the practical.

교육 기관: venkatpullela

2016년 11월 17일

Course is really good. Assignments are taking too much time if you want to do the course rally fast, with questionable learning value.

교육 기관: Sergio D H

2016년 7월 22일

AWESOME COURSE!! Carlos and Emily are incredible teachers and the course contents are truly informative and well-paced for beginners.

교육 기관: stephane d

2021년 2월 20일

Really a great course!

Thanks to Emily and Carlos!

I still hope there will be more courses after the 4 courses of this specialization.

교육 기관: Nitin D

2018년 12월 18일

Excellent lessons on this important topic Classification. I think all major areas were explained quite nicely, with proper examples.