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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
3,275 個評分
543 條評論

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

SM

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 :)

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!

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351 - Machine Learning: Classification 的 375 個評論(共 511 個)

創建者 王曾

Nov 27, 2017

very good

創建者 Muhammad H S

Nov 02, 2016

Excellent

創建者 Joshua C

May 03, 2017

Awesome!

創建者 Roberto E

Mar 01, 2017

awesome!

創建者 Isura N

Dec 28, 2017

Hoooray

創建者 Shashidhar Y

Apr 02, 2019

Nice!!

創建者 Subhadip P

Aug 04, 2020

great

創建者 Nicholas S

Oct 07, 2016

Great

創建者 李真

Mar 06, 2016

great

創建者 Dr S J

Jun 19, 2020

good

創建者 AMARTHALURU N K

Nov 24, 2019

good

創建者 RISHI P M

Aug 19, 2019

Good

創建者 Akash G

Mar 10, 2019

good

創建者 xiaofeng y

Feb 06, 2017

good

創建者 Kumiko K

Jun 05, 2016

Fun!

創建者 Arun K P

Oct 17, 2018

G

創建者 Navinkumar

Feb 23, 2017

g

創建者 MARIANA L J

Aug 12, 2016

The good:

-Good examples to learn the concepts

-Good organization of the material

-The assignments were well-explained and easy to follow-up

-The good humor and attitude of the professor makes the lectures very engaging

-All videolectures are small and this makes them easy to digest and follow (optional videos were large compared with the rest of the lectures but the material covered on those was pretty advanced and its length is justifiable)

Things that can be improved:

-In some of the videos the professor seemed to cruise through some of the concepts. I understand that it is recommended to take the series of courses in certain order but sometimes I felt we were rushing through the material covered

-I may be nitpicking here but I wish the professor used a different color to write on the slides (the red he used clashed horribly with some of the slides' backgrounds and made it difficult to read his observations)

Overall, a good course to take and very easy to follow if taken together with the other courses in the series.

創建者 Hanif S

Jun 02, 2016

Highly recommended course, looking under the hood to examine how popular ML algorithms like decision trees and boosting are actually implemented. I'm surprised at how intuitive the idea of boosting really is. Also interesting that random forests are dismissed as not as powerful as boosting, but I would love to know why! Both methods appear to expose more data to the learner, and a heuristic comparison between RF and boosting would have been greatly appreciated.

One can immediately notice the difference between statistician Emily, who took us through the mathematical derivation of the derivative (ha.ha.) function for linear regression (much appreciated Emily!), and computer scientist Carlos, who skipped this bit for logistic regression but provided lots of verbose code to track the running of algorithms during assignments (helps to see what is actually happening under the hood). Excellent lecturers both, thank you!

創建者 Amilkar A H M

Nov 27, 2017

It's a great course, but the programming assignments are a little too guided. That is good, to some extend, as it allows you to focus on the concepts, but at the same time, it leave little space for actually practicing your coding skills. I know they said from the beginning that this course was not focused on the implementation of the algorithms, however, how are you going to be able to use what you've learned without knowing how to implement the algorithms on your own.

When it comes to coding, nothing replace implementing the algorithms yourself. That is my only complaint. Other than that, it's great. I loved it. The concepts were well explained and they covered a lot of material. I wish they had spent more time in certain topics, but I guess this is just an introduction. Anyway, take this course by any means if you have some programming experience and have little to no machine learning knowledge.

創建者 Daniel C

Apr 25, 2016

This series is taught by Emily and Carlos. Course 2 was Emily and this course 3 is Carlos. Carlos takes a more practical approach by showing how things are related using pictures, trial and error, what happens when we do this vs. that. Emily on the other hand dives down into the math and actual facts. I feel Emily is more difficult overall - but once I got through it, I had a better foundation and intuition as to how things work and better overall understanding. So - giving this class 4 stars as compared to Emily's class that is 5 stars. I feel if they would mix it with Emily doing the math immediately followed by Carlos explanations it would be best. Finally - I don't feel this course on classification had as much content. We could've done more.

創建者 Jaiyam S

Apr 24, 2016

Thank you Prof. Carlos for this amazing course. You covered the topics in a very easy to understand way and the course was full of cool applications and humor! The only downside that I felt was that the programming assignments sometimes felt too easy. Even as a complete Python novice (I started learning Python with the first course), I felt the programming assignments could have been made more interesting. But in the larger scheme of things it doesn't matter because the course was really well taught and easy to understand. I'm really looking forward to the next course! :)

創建者 Lech G

Apr 26, 2016

Not as good as the Regression Course, but still very good.

While I appreciate prof Guestrin's enthusiasm, I missed a little rigor and mathematical depth of the Regression's course by prof. Fox.

I learned a lot, but I feel that regression clicked with me a little better than classification.

But that's probably me.

In either case, the whole series are awesome so far, better, in my opinion, than Anrdrew Ng's ML course on coursera,

A small suggestion would be to switch the main toolset from the Graphlab to something more common, like Sci-kit learn and Pandas.

創建者 Alessio D M

Apr 17, 2016

The course is definitely high-quality and the topics are covered in a good way. I'm not giving 5 stars because I would have expected SVMs and neural networks. Mentioning the many different algorithms for learning decision trees would have been nice, without necessarily focusing on each of them in depth. An entire week spent on precision/recall seems a little bit too much, without touching other metrics like F-score. Overall though a very nice course for beginners, and it definitely gives a good sense of classification challenges and approaches.

創建者 SUBIKESH P S

Jun 11, 2020

This course was very useful for learning machine learning, as this describes classification models deeply and also about other important ML techniques like Online Learning, handling missing data, precision-recall, etc. The weekly programming assignments were elaborate and explained all the topics nicely. The classes were also made interesting by Mr. Carlos by cracking puns in between.

The only problem I face is that using turicreate over sklearn. Since turicreate is depreciated for windows, it's hard to complete programming assignments.