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Learner Reviews & Feedback for Machine Learning: Classification by University of Washington

4.7
2,768 Bewertungen
463 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

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

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1 - 25 of 433 Reviews for Machine Learning: Classification

von Lewis C L

Jun 13, 2019

First, coursera is a ghost town. There is no activity on the forum. Real responses stopped a year ago. Most of the activity is from 3 years ago. This course is dead.

Two, this course seems to approach the topic as teaching inadequate ways to perform various tasks to show the inadequacies. You can learn from that; we will make mistakes or use approaches that are less than ideal. But, that should be a quick "don't do this," while moving on to better approaches

Three, the professors seem to dismiss batch learning as a "dodgy" technique. If Hinton, Bengio, and other intellectual leaders of the field recommend it as the preferred technique, then it probably is.

Four, the professors emphasize log likelihood. Mathematically, minus the log likelihood is the same as cross-entropy cost. The latter is more robust and applicable to nearly every classification problem (except decision trees), and so is a more versatile formulation. As neither actually plays any roll in the training algorithm except as guidance for the gradient and epsilon formulas and as a diagnostic, the more versatile and robust approach should be preferred.

The professors seem very focused on decision trees. Despite the "apparent" intuitive appeal and computational tractability, the technique seems to be eclipsed by other methods. Worth teaching and occasionally using to be sure, but not for 3/4 of the course.

There are many mechanical problems that remain in the material. At least 6 errors in formulas or instructions remain. Most can be searched for on the forum to find some resolution, through a lot of noise. Since the last corrections were made 3 years ago, the UW or Coursera's lack of interest shows.

It was a bit unnecessary to use a huge dataset that resulted in a training matrix or over 10 billion cells. Sure, if you wanted to focus on methods for scaling--very valuable indeed--go for it. But, this lead to unnecessary long training times and data issues that were, at best, orthogonal to the overall purpose of highlighting classification techniques and encouraging good insights about how classification techniques work.

The best thing about the course was the willingness to allow various technologies to be used. The developers went to some lengths to make this possible. It was far more work to stray outside the velvet ropes of the Jupiter notebooks, but it was very rewarding.

Finally, the quizzes were dependent on numerical point answers that could often be matched only by using the same exact technology and somewhat sloppy approaches (no lowercase for word sentiment analysis, etc.). It does take some cleverness to think of questions that lead to the right answer if the concepts are implemented properly. It doesn't count when the answers rely precisely on anomalies.

I learned a lot, but only because I wrote my own code and was able to think more clearly about it, but that was somewhat of a side effect.

All in all, a disappointing somewhat out of date class.

von Christian J

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

von Saqib N S

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!

von 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.

von Naman M

Jul 09, 2019

you can't find a better course on machine learning as compared to this one. Simply the best course on coursera

von Jafed E

Jul 06, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

von Gaurav B

Jul 04, 2019

Explaination Is Not good I have to take help from other courses

von Thuc D X

Jun 28, 2019

Sometimes the assignment description was hard to follow along. Overall, the course equips me a good understand and practical skills to tackle classification tasks.

von Aakash S

Jun 15, 2019

Amazing Explanation of every thing related to Classification.

Thanks a lot for the course.

von Yufeng X

Jun 14, 2019

The lecture is super. The exams could be more challenging-:)

von sudheer n

Jun 12, 2019

The way Carlos Guestrin explains things is exquisite. if basics is what is very important to you, and can learn code implementation and libraries from other sources, this is the go to course

von 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.

von Md s

Jun 09, 2019

awesome course , have learned lot of stuff

von Dohyoung C

Jun 04, 2019

Great ...

I learned quite a lot about classification

von Karthik M

Jun 01, 2019

Excellent course and the instructors cover all the important topics

von Vibhutesh K S

May 22, 2019

It was a very detailed course. I wished, doing it much earlier in my research career. Great insights and Exercises.

von Gaurav C

May 22, 2019

Would have loved even more had Carlos explained his students gradient boosting as well. I liked the way of his taught in lectures.

von Miguel Á B P

May 21, 2019

Excellent course!

von akashkr1498

May 19, 2019

good course but make quize and assignment quize more understandable

von MAO M

May 07, 2019

lots of work. very good for beginners

von YASHKUMAR R T

May 03, 2019

This course will provide you clear and detailed explanation of all the topics of Classification.

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 Neelkanth S M

Apr 08, 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 Shashidhar Y

Apr 02, 2019

Nice!!

von Ashish C

Mar 31, 2019

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