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Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Classification von University of Washington

4.7
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3,436 Bewertungen
574 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

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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401 - 425 von 542 Bewertungen für Machine Learning: Classification

von Karen B

Jul 30, 2016

The course covers many aspects of classification, with each section building on the one before. The lectures cover the theory, with a little bit of practical information, fairly well. The instructor tries to make the lectures interesting, and they are.

The quizzes seem designed both to reinforce what the lectures taught and to expand on them. The quizzes, particularly those based on programming, could use proofreading by someone newer to the subject.

von Sacha v W

Nov 10, 2018

The course is well structured and very well explained. The structure is step by step increasing the the complexity. The programming exercises are excellent. I really appreciate the humor and passion of Carlos in teaching the material and his ability to explain complex matters with simple examples. The only drawback is that the course uses python packages that are less familiar. That is why I audited the course and worked with pandas and sklearn.

von Michael C

Apr 07, 2016

The course provides an overview on classification methods in machine learning.

The lectures are clear and easy to understand due to the quality of the slides and of the explanations.

The limit of this course lies in the assignments: too easy if done with the provided notebooks and tools. Sometimes impossible to do with different tools (the suggested machine learning package is free for educational purposes, but otherwise it needs a license).

von Shahin S

Sep 15, 2016

The lectures are very well prepared and clear. With regards to the assignments: I think it will be nice to design the assignments in a way that allows people to use the language and libraries they prefer as much as possible. I would also prefer to write more of the coding assignments by myself, instead of trying to fill in the blanks in some pre-written code and complete them. That will help the students to learn a lot more.

von MITUL T

Sep 02, 2020

This course is well paced. Toughness of assignment and quizzes are moderate and are very conceptual. The only thing this course lacks is it only teaches basic stuff and you need to refer other sources if you are interested to study some advance techniques. This course builds a strong foundation of math and statistics in ML field. If you are struggling to understand math behind all algorithms I do recommend this course.

von Naveendhar

Aug 09, 2019

Last portion was a little difficult to relate to why we started this move for large datasets in the first place. I had to keep going to the fact that I am going to be handling large datasets. Like the use cases. simple and effective. The quizzes were simple and the graph questions were really helpful in gauging my understanding of math behind these models.

von Stefano T

Mar 15, 2016

The contents are very interesting and well explained. Nevertheless, unlike the Regression module, the current one suffers of some technical problem, like slides not well formatted, noisy audio in some video, weekly work load not perfectly calibrated. Despite all this, if you are interested in the subject, you will definitely love this course!!!

von Marku V d S

Dec 23, 2017

I loved the course. Carlos Guestrin is an excellent and engaging professor that really captivate me to work hard to accomplish the assignments.

I just suggest that the assignments should be divided into small pieces to be taken as long the week is accomplished. I felt bad some weeks that had a lot of videos to watch before the first assignment.

von Lorenzo L

Aug 31, 2018

Good, funny and super-clear professors introduce you to the main classification techniques out there (except for neural networks). Great if you are approaching this field and want to know more before deciding if you really want to invest a lot in it. 4 stars because it would have been better with more popular python packages than GraphLab.

von Craig B

Dec 19, 2016

Not as evenly paced as the first two courses. Also some material was covered at a very high level, whilst I found that some explanations did not immediately build on my understanding gained through the foundation course, but rather confused it. Still a worthwhile course nonetheless. I look forward to the rest in the specialisation.

von Nitin M

Nov 07, 2019

The course is perfect for people who want to gain in-depth knowledge of classification algorithms but exercise descriptions are vague. I found trouble understanding the flow of assignments. Also, Bagging and Gradient Boosting techniques were not covered under ensembles. Overall, the course is awesome.

von mahesh

Aug 04, 2018

I can give a five star for this course, but removed one star cause graphlab api annoyed me a lot of times. The theory covered in this is course is good. The programming assignments are well structured but if api's like pandas, numpy, scikit learn were used it would have made my life easy.

von Dilip K

Dec 21, 2016

Excellent course that I have already recommended to a couple of people. Only annoying thing is the continued inconsistency between the Graphlab version and other versions (I use sframe with python - no graphlab) - some of the instructions are less than clear and needlessly waste time.

von KANDARP B S

Mar 02, 2017

The course 3 got pretty technical pretty soon. Enjoyed the first 2 courses without feeling overwhelmed. But course 3 was challenging. I suppose building the expectation of what is to come can reduce the challenge and lead to faster and more number of course completions.

von Aleksander G

Apr 11, 2016

Just one comment about how the course could be improved: the assignments should be more hands-on with fewer pieces of code written in advance. I say this is even though I am not a skilled programmer. The assignments would be a bit harder, but also a bit more rewarding.

von Jaime A C B

Sep 12, 2016

Sometimes is difficult to understand the concept behind Classification because some videos are more practical than theorical, I mean it could be better to start the video explaining some concepts and then show and explan some samples and theorical issues.

Thanks.

von Nicolas S

Jan 02, 2020

The course itself is well structured and introduce gradually the complexity. Unfortunately, the exercises requires the use of a specific library, instead of scikit-learn and numpy. Furthermore, they also required Python 2, while Python 3 is now widely used.

von Martin B

Apr 11, 2019

As with all the courses in this specialization: great production values, excellent tuition. Useful assignments, even though the reliance of Graphlab Create is a bit of a drag. I also would have liked to see some discussion of Support Vector Machines.

von J N B P

Oct 09, 2020

This course covers all the core algorithms used in Classification models. If you have a basic understanding of machine learning, this course can help you build your understanding of classification on a deeper level.

von Uichong D L

Sep 17, 2017

Using discontinued Graphlab in the programming assignment is a minus and low activities in the forum makes hard to find assistance from the communities or mentors but the course material itself is just great.

von João S

Apr 18, 2016

Very good content, very well explained... great course. Classification its a very broad topic but i think this is great introduction.

The hands on where kinda on the easy side... but very interesting.

von David F

Aug 07, 2016

Not as good as the previous courses in this specialization - I agree with those who have noted that this one seemed a little rushed. However, these are still the best courses I've found on Coursera.

von Ahmed N

Feb 23, 2018

Great knowledge about machine learning fundamentals, More math illustration needed though it's great knowledge and very great basics about different machine learning algorithm used in reality

von Eric M

Apr 15, 2017

Extremely clear and informative. Good introduction to ML. I felt the labs could have had us write a little more of our own code, and would have been better to use non-proprietary libraries.

von Dawid L

Mar 20, 2017

Presented content is rather clear and instructors are rather easy to follow. Only the assignments are often confusing as there are questions which refer to missing content.