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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)....




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 :)



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!


Machine Learning: Classification: 201 - 225 / 572 レビュー

by Yoshifumi S


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

by Japneet S C


Course is very good. Concepts are explained in a very simple way.

by dragonet


thank you every much, every helpful! ~i will repeat several time~

by Mark W


Fantastic Lecturers and very interesting and informative course

by D D


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

by Eric N


Excellent online teaching with clear and concise explanations!

by Parab N S


Excellent course on Classification by University of Washington

by Mohd A


Learning is fun when you have professors like Carlos Guestrin.

by Ali A


the course material is great but the assignments are not good

by clara c


This course was great! I really enjoyed it and learned a lot.

by Yufeng X


The lecture is super. The exams could be more challenging-:)

by Sarah W


Great course! Learned so much! So excited to use this stuff!

by Tony T


funny and enthusiastic lecturer make a dry subject more fun.

by Simbarashe M


l know a knew way to train the models taught in this course

by Isaac B


Excellent course. Practical understanding of classification

by Ali A


So far it is a mazing. I will rate at the end of the course

by Kartik W


A must do course for all the machine learning enthusiasts.

by Koen O


Excellent course for learning the basics on classification

by Chao L


Nicely formatted. And it's quite intuitive and practical.

by Patrick P


Very good and and informative to start with this subject.

by vacous


very nice material covering the basic of classification.

by Xuan Q


Super useful and a bit of challenging! Really enjoy it.

by Carlos L


The contents are really clear and professors are great!

by Freeze F


This lecture gave a great start for me into ML . :) :)

by Sudip C


Very detailed, Liked optional sections also. Loved it.