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

4.7
2,902 個評分
482 個審閱

課程概述

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)....

熱門審閱

SS

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!

CJ

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

篩選依據:

251 - Machine Learning: Classification 的 275 個評論(共 450 個)

創建者 Joshua C

May 03, 2017

Awesome!

創建者 Manuel S

Sep 11, 2016

Great course!

創建者 Simon C

Oct 28, 2016

Great content and exercises which facilitated understanding of very complex concepts.

創建者 Lei Q

Mar 16, 2016

Excellent theory and practice(coding)!

創建者 Renato R S

Aug 27, 2016

All the basics - and much of the advanced stuff - is presented, in a coherent and inspired way. Thanks for crafting such a course.

創建者 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!

創建者 Binil K

Jul 30, 2016

Nice Course, very much helpful and reccomended

創建者 Nikolay C

Mar 16, 2016

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.

創建者 Benoit P

Dec 29, 2016

This whole specialization is an outstanding program: the instructors are entertaining, and they strike the right balance between theory and practice. Even though I consider myself quite literate in statistics and numerical optimization, I learned several new techniques that I was able to directly apply in various part of my job. We really go in depth: while other classes I've taken limit themselves to an inventory of available techniques, in this specialization I get to implement key techniques from scratch. Highly, highly recommended.

FYI: the Python level required is really minimal, and the total time commitment is around 4 hours per week.

創建者 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.

創建者 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)

創建者 Nicholas S

Oct 07, 2016

Great

創建者 Tuan L H

Dec 06, 2016

Great course, easy to follow, higly recommended!

創建者 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!

創建者 Jan L

Aug 02, 2017

Just great

創建者 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

創建者 Thuong D H

Sep 23, 2016

Good course!

創建者 Jair d M F

Apr 21, 2016

Very Good!

創建者 Gustavo d A C

Apr 23, 2017

It was a nice course. I could learn many new techniques and algorithms. Very exciting !!

創建者 Lixin L

May 07, 2017

really good course. thanks

創建者 Niyas M

Oct 29, 2016

Amazing course! Packed with insights, reasoning and Carlos's humor and wit. Highly recommended for novices (along with the Machine Learning Foundations course).

創建者 Krzysztof S

Jun 06, 2017

great course

創建者 Usman

Nov 13, 2016

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

創建者 Renato V

Jul 13, 2016

A very good course, with effective intuitive explanations of what the algorithms are supposed to achieve and how. The exercises in Python help understand the topic and fix it in memory.

創建者 Ashish

Oct 26, 2016

I appreciate the way Emily and Carlos explain the concepts. Its very intuitive for beginners and optional sections give further details. The datasets used in programming assignments are taken from real world examples.

Overall an excellent course and really looking forward to completing the series.

Kudos to Carlos, Emily and the team.