Chevron Left
返回到 Machine Learning: Classification

學生對 华盛顿大学 提供的 Machine Learning: Classification 的評價和反饋

2,899 個評分
481 個審閱


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


201 - Machine Learning: Classification 的 225 個評論(共 450 個)

創建者 Pardha S M

Jun 02, 2017

All the quiz and programming assignments prepared such away that student can easily get into the workflow, concentrating more on concepts without taking much overhead of programming yet need to think rigorously while writing that small portion of "YOUR CODE" parts on couple of occasions

創建者 Ayush K G

Nov 01, 2017

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

創建者 ChangIk C

Oct 25, 2016

Learned a lot recommend!

創建者 Fabio P

Apr 18, 2016

Very interesting topic with some advanced topics covered. It really shows how to use machine learning in the real world.

創建者 Anurag U

Jan 16, 2017

Best source to learn classification techniques

創建者 李真

Mar 06, 2016


創建者 Jing

Aug 14, 2017

Better than the regression course

創建者 Le L

May 02, 2017

Lots of knowledge

創建者 Samuel d Z

Jul 10, 2017

AWESOME!!! Very well structured. Concepts are explained in small and short videos which focus on one thing at the time. Unnecessary clutter is removed and deep dives can now be done with this solid foundation. Also the Python programming part teaches so much and again, only asked to program the essentials and non essentials or "special tricks" are done, so you can see and learn from them without having to search on the web. THANKS.

創建者 Konstantinos P

Mar 28, 2017

The context and the structure of the course is absolutely perfect. Also, Carlos is the perfect professor!

創建者 japneet s c

Feb 06, 2018

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

創建者 Josef H

Nov 27, 2016

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

創建者 Freeze F

Jun 07, 2016

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

創建者 Srinivas J

Nov 12, 2016

truly enjoyed this course and recommended to my colleagues as well.

創建者 venkatpullela

Nov 17, 2016

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

創建者 Tewende J E K

Jul 24, 2016

intuitive, clear and practical. The best explanation I found so far !

創建者 Siddharth S

Jan 09, 2018

Excellent course and all the concepts have been explained very simply and with an element of fun.

Many thanks to Emily and Carlos...

創建者 JOSE R

Nov 18, 2017

Very interesting. It's easy to understand. Thanks

創建者 Shuang D

Jun 29, 2018

nice course!

創建者 Yang X

Oct 29, 2017

Very helpful!

創建者 Rahul M

Nov 12, 2017

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

創建者 Alessandro B

Oct 31, 2017

nice, clear engaging ...and useful

創建者 Do A T

Nov 15, 2017

very useful

創建者 Babak P

Jun 28, 2018

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

創建者 Trinh Q N

Jan 29, 2018

Give me a good understanding of Classification