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Machine Learning: Classification に戻る

ワシントン大学(University of Washington) による Machine Learning: Classification の受講者のレビューおよびフィードバック



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)....



Oct 16, 2016

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!


Jan 25, 2017

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses


Machine Learning: Classification: 226 - 250 / 453 レビュー

by Tripat S

Jun 24, 2016

This is the best course ever that can happen in ML...I did not know anything, but after taking this specialization, my understanding of ML has dramatically improved

Would recommend without any reservation - Prof Gustrin and Prof Fox are the best!!!

by Evgeni S

Jun 11, 2016

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

by Shaowei P

Mar 31, 2016

great course, would have been even more great if there are more details on how to use boosting for kaggle

by Pandu R

Apr 20, 2016

Worth the wait.

by Richard L

Oct 15, 2016

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

by Sara E E

Mar 29, 2018

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.

by Krishna C

Sep 16, 2017

Great Course

by Sanjay M

Jun 30, 2017

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

by Willismar M C

Nov 19, 2016

Amazing Course Module, I learned a lot of concepts for classifications using Decision Trees, Logistic Functions, Boosting, Ensemble and way to attack problems. Also a lot of coding with Graphlab, I personally like to program by my own but I also appreciating the tool for the class and comparing my skills with other tools. Very cool ! Nice Class

by Sundar J D

Apr 23, 2016

Overall a great course and has a very good instructor. Teaches you all the fundamentals behind classification algorithms and models. Contains very good assignments/projects that make you implement the models yourself to get a much better understanding of the concepts.

by Yoshifumi S

May 08, 2016

As always in this specialization, tough course but so practical !!

by m w

Dec 24, 2017

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

by Jenny H

Jan 01, 2017

All courses in this series are organized and taught in an extremely efficient manner. I have learned so much out of them and they have helped me with my current job and my next job search!

by Alex L

Mar 08, 2016

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.

by Pawan K S

May 15, 2016

Nice course with appropriate amount of detail in it! Covers tough mathematical aspect for those who are interested in it.

by Joshua A

Sep 20, 2016

Very thorough and engaging. Optional material allowed the more curious to learn a great deal about the topics. Simple, hands-on approach to classification algorithms. Highly recommended!

by Filipe G

Apr 02, 2016

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

by Jair d M F

Apr 21, 2016

Very Good!

by Isaac B

Nov 20, 2016

Excellent course. Practical understanding of classification

by Manuel T F

Jul 21, 2017

Really great course. Well done!

by Wenxin X

Mar 26, 2016

This specialization overall is pretty good. Personally I feel like Classification talks more about concepts and important ideas and requires less on coding comparing to Regression. Learned a lot! Love Carlos and Emily!

by Maria C

Mar 08, 2016

One of the best online machine learning courses I have taken. Excellent explanation of many techniques on Classification. A great combination of theory and hands-on examples. Thank you, Professors Fox & Guestrin.

by Andrew M O

Jun 15, 2016

I came here to learn. I learned.

by Ganesan P

Feb 06, 2017

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

by Uday A

Jun 15, 2017

Great learning experience. Thanks to Carlos and Emily! Loving every bit of this specialization. :)

It would help if there could be a small introduction to other types of classifiers (Naive Bayes, SVM etc), atleast pointing the student to external resources to try them out.