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Bewertung und Feedback des Lernenden für Machine Learning: Classification von University of Washington

4.7
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3,670 Bewertungen

Über den Kurs

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)....

Top-Bewertungen

SM

14. Juni 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

15. Okt. 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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526 - 550 von 575 Bewertungen für Machine Learning: Classification

von Francesco

15. Nov. 2019

The material is good, but the choice of using GraphLab Create is a poor one. It's not used in the industry and it's poorly supported. I had issues installing it both via command line and via the installer, so I ended up using the AWS machine. But that has it's own drawbacks, such as the slowness and the setup time.

von Nitzan O

25. Apr. 2016

The course is interesting and well taught. The professor is very enthusiastic and it makes the course fun to watch. The problem in my opinion is that the content is too superficial. It's completely lack of mathematical background and the programming exercises are sometimes no more than copy paste.

von ANIMESH M

4. Sep. 2020

The course is up to the mark but what i felt missing is about the coding . They didn't focus on implementation tasks simply gave the notebooks for the assignments.

Also S.V.M and random forest classifiers are missing.

From my side concluding all the experience , i will give a 6.5 out of 10.

von Kumar B

4. Okt. 2017

This course covers the basics of classification very well, but I would have liked optional sections on more advanced topics. Some of the quiz questions were a bit confusing. It would have been good if the exercises also dealt with unbalanced data sets in more detail.

von Neelkanth S M

8. Apr. 2019

The content is good but completing assignments is a real pain because they choose to deploy a unstable proprietary python library, which gives hard time installing and running (as of Q1 2019). The entire learning experience is marred by this Graphlab python library.

von D B

13. Juni 2018

Pros: Absolutely fantastic theory explanations. Establishes solid fundamentals. Cons: The bugs in test/notebooks could have not been rectified with new ones. Demands searching in discussion forum every time. Would highly recommend for starters!

von Eric A J C

5. Aug. 2021

The videos were excellent, and the extra material to delve in deeper in the subject were very nice. However, the programming assignments were mostly chunks of ready-made code, so not much is left to the learner.

von ANGELICA D C

22. Sep. 2020

Finalizo siendo muy confuso. El conocimiento de los videos opcionales no se le daba seguimiento, hasta el final en las tareas es cuando se usaba pero ya estaba fuera de contexto y era difícil entender.

von Supharerk T

6. Juli 2016

All of the courses lecture are great until it reaches week 5 where it's really hard to catch, the programming assignment doesn't give enough hints and lecture in this topic doesn't help much.

von nazar p

29. Juni 2017

While courses 1 and 2 of this specialization were quite good, I find this one a bit sparse on content. I think this course could be easily compressed into 2-3 weeks instead of 7.

von Rohit J

12. Mai 2016

A lot of interesting parts of the course are available as optional and a lot of the difficult parts of the coding exercises are provided to you - the challenge is not there. :/

von Ilan S

23. Nov. 2016

The videos were pretty goods. But a bit too slow and easy. The assigments were ok, but too guiding. Also there were too much reimplementation of algorithm

von Rahul S

17. Juni 2020

Too much confusion, I face too much problem with this course. much confusion if you use different packages like sklearn.

von Lawrence G

19. Mai 2016

The course content seemed to be rushed out, as a result, the quality is not as good as the first two.

von Tu L

27. Juni 2018

Why don't you guys talk about ID3 or CART algorithm at all? This one is too basic.

von Mounir

19. Juni 2016

Exercises for Scikit-learn users were not organised.

Course took too long to start

von Pier L L

26. März 2017

Nice course but I would have expected more techniques (SVM for instance)

von Dmitri B

6. Juni 2017

Theory Quizes are good, but programming assignment not so good for me.

von Ashish C

31. März 2019

more topics like deep learning, neural networks need to be introduced

von Matt T

12. Apr. 2016

Good, but overemphasizes niche software product (graphlab).

von Virgil P

18. Feb. 2018

The exercises/assignments are far too simple

von 陈弘毅

3. Feb. 2018

too simple

von Deleted A

13. Aug. 2020

good

von Omkar v D

14. Aug. 2018

.

von Rohan G L

29. Aug. 2020

I leave 2 stars as I learned a lot of new information and methods, and the theory and math behind them.

You will learn about Data Science and Machine Learning, but not much about Python.

The course is pretty much abandoned and outdated. Sframes and Turicreate packages (instructor's creations) are used instead of more universal packages. Installation in the beginning took some time and research. Many of the assignments have errors and bugs in the code that have not been updated. Forum assistance is abysmal for clarification or deeper questions. Many links are dead.

There are many times in the lectures where the instructors are writing several sentences in their handwriting on their notes instead of having the text ready to appear.

I would suggest using this course and series as a supplement to other information one as learned, not as an introduction for initial understanding. I found myself frustrated too many times.