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

3,384 個評分
564 條評論


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)....



Jun 15, 2020

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 :)


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!


76 - Machine Learning: Classification 的 100 個評論(共 533 個)

創建者 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!

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 Thomas E

May 12, 2016

A bit easy to get through the exercises bur otherwise a very enlightening and inspiring course. - This is btw a positive review if anybody should be in doubt after taking this course :)

創建者 Shaik R

Jul 12, 2019

Best Machine Learning classification course by far....

each aspect is explained in detail..but forum responses can be improved..

Great course for machine Learning beginners... loved it.

創建者 Krisda L

Jun 24, 2017

Great course. I learned a lot about Classification theories as well as practical issues. The assignments are very informative providing complimentary understanding to the lectures.

創建者 Michele P

Aug 23, 2017

The course starts slow, but it gets more interesting from week 2. The assignments are more challenging than in Regression, but I have really enjoyed it. I highly recommend it!

創建者 Dave M

Apr 30, 2020

Good Class. Program assignment have a bit too much hand holding, which made them easier and less useful than they might have been if they were allowed to be more challenging.

創建者 Dhritiman S

Feb 09, 2017

These courses have been a perfect mix of theory and practice. Looking forward to the final two courses in the specialization getting released at some point in the future :)

創建者 Phil B

Feb 13, 2018

Excellent overview of the most commonly used Classification techniques, providing the wireframe for us to write our own algorithms from scratch. Really enjoyed this one.

創建者 Kuntal G

Nov 03, 2016

Great course with detail explanation ,hands-on lab along with some advance topic. Really a great course for anyone interested in the field of real world machine learning

創建者 Shazia B

Mar 25, 2019

one of the best experience about this course i gained I learned a lot about machine learning classification further machine learning regression thanks a lot Coursera :)

創建者 Fakrudeen A A

Sep 15, 2018

Excellent course - teaches linear, logistic regression and decision trees. It also teaches the most important concept of precision-recall. Overall highly recommended.

創建者 Cenk B

Apr 28, 2020

It is technically and mathematically detailed and well-organized course and the assignments are also make me understand better about the algorithms and use details

創建者 Marcus V M d S

Oct 16, 2017

Another great course from this specialization. Tremendous effort in making the notebooks and assignments. I just think there could be recommended readings also.

創建者 ZHE C

Mar 26, 2017

effective teaching and practice about decision tree, boosting, and logistic regression. Could have a little more practice on gradient boosted tree/random forest

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 Leon A

Mar 10, 2016

Course material selection, pace and presentation are all well thought out. This sequence of courses in the Machine Learning specialization is truly exceptional.

創建者 lokeshkunuku

Jun 12, 2019

its been 3 weeks I started this course it was so nice and awesome. the lectures explaination and the ppt all were well crafted and easy to pick and understand.

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 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

創建者 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.

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 Farrukh N A

Feb 10, 2017

I found carols to be the best instructor in machine learning domain, he presented the algorithms and all core machine learning concepts in really great way.

創建者 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.

創建者 Jane z

Jan 26, 2020

The hands-on approach is excellent. Not only I learned ML / Classification, I was able to practice Python skills and statistical skills as well.