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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: 51 - 75 / 441 レビュー

by Alexander S

Aug 07, 2016

one of the best courses.

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.

by Thuong D H

Sep 23, 2016

Good course!

by Saravanan C

Jul 08, 2017

Excellent effort by the tutors to simplify and motivate the learning process (it kept me engaged) One shouldn't forget that this is just a start NOT an end of acquiring the programming skills as it spoon feeds majority of the supportive (or) actual code!! (so please open a blank notebook and write ALL pieces of needed code as well)

by Nicholas S

Oct 07, 2016


by Tuan L H

Dec 06, 2016

Great course, easy to follow, higly recommended!

by Richard N B A

Mar 09, 2016

A great course! Well presented, does not shy away from the mathematics (very nice optional units that go into more detail for the interested student!), keeps focus on the material and maintains the structure and feel of the specialization as a whole. It's great that we get to actually implement some of the algorithms. Strongly recommended!

by Jan L

Aug 02, 2017

Just great

by André F d A F C

Jul 25, 2016

Excellent course.

by Bharat J

Jan 19, 2018

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

by Paul C

Aug 13, 2016

This Machine Learning class and the rest of the Machine Learning series from the University of Washington is the best material on the subject matter. What really sets this course and series apart is the case-base methodology as well as in-depth technical subject matter. Specifically, the step through coding of the algorithms provides key insight that is seriously missed in other classes even in traditional academic settings. I highly encourage the authors and other Coursera publishers to continue to publish more educational material in the same framework.

by Arash A

Dec 01, 2016

Learned a lot and enjoyed even more. Thanks!

by Pankaj K

Sep 25, 2017

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

by Sandeep J

Sep 04, 2016

Its s great course

by D D

Oct 16, 2016

Nice videos. Learned a lot. Also videos good for future review.

by Shiva R

Apr 16, 2017

Exceptional and Intutive

by OG

Aug 03, 2016

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.

by Andre J

Mar 18, 2016

These Machine Learning classes have been fantastic so far, really enjoying them. Very good coverage of topics and challenging exercises to drive home the learning. The effort put into developing the classes has been superb and I look forward to the rest of the specialization.

by Sandeep K S

May 07, 2016

awesome course awesome teachers

by Luis E T N

Jul 04, 2017

Excelent! Congrats!

by Dwayne E

Dec 21, 2016

Awesome course learned alot

by Henry H

Nov 18, 2016

Very clear and easy to understand.

by Albert V d M

Mar 08, 2016

Very instructive, you learn a lot.

by Marios A

Mar 08, 2016

The course is really well structured and gives a solid understanding in the latest approaches in Machine Learning. However I would also like to see in this course more sophisticated math, because it matters and I think there are important.

by Kumiko K

Jun 05, 2016