Chevron Left
Zurück zu Machine Learning: Classification

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

2,924 Bewertungen
485 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)....



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!


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

Filtern nach:

1 - 25 von 453 Bewertungen für 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


Oct 02, 2019

It will definitely help you in understanding the basics to dept of most of the algorithms. Even though you are already aware of most of the things covered elsewhere related to Classification, this course will add up up a considerable amount of extra inputs which will help to understand and explore more things in Machine learning.

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 Feng G

Jul 12, 2018

Very helpful. Many ThanksSome suggestions:1.Please add LDA into the module.2.It is really important if you guys can provide more examples for pandas and scikit-learn users in programming assignments like you do in regression module.

von Alex H

Feb 08, 2018

Relying on a non-open source library for all of the code examples vitiates the value of this course. It should use Pandas and sklearn.

von Xue

Dec 15, 2018

Very good lessons on classification.

von Manuel G

Jan 01, 2019

Really awesome course. Nice balance between practical uses, theory, and implementation projects. It's good they kept the "optional" videos for the more detailed discussion instead of just removing that material. Totally recommend it.

von Nitin D

Dec 18, 2018

Excellent lessons on this important topic Classification. I think all major areas were explained quite nicely, with proper examples.

von Zhongkai M

Feb 12, 2019

Great course, provided details that not show in others' and textbooks.

von Jialie ( Y

Feb 08, 2019

It is really useful and up to date.

von parv j

Mar 03, 2019

Brilliant course!

von Akash G

Mar 10, 2019


von Reinhold L

Mar 21, 2019

Very good course for classification in machine learning - top presentation documents - very well structured and practical

von 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 :)

von Nidal M G

Dec 04, 2018

very good

von Gaurav G

Dec 27, 2018

Good Course!!

von Shashidhar Y

Apr 02, 2019


von Arslan a

Feb 18, 2019

the person who wants to start career in machine learning must take this course! Its awsome :)


Jul 18, 2018

Very clear and useful course, excellent.

von Naimisha S

Jul 30, 2018

Availability of the Ipython notebook makes it easy to solve the Quizzes which has step by step explaination

von Pandu R

Apr 20, 2016

Worth the wait.

von Richard L

Oct 15, 2016

Great course. The lectures and programming assignments have been extremely beneficial to help me get a basic foundation of ML classification.

von Krishna C

Sep 16, 2017

Great Course

von Yoshifumi S

May 08, 2016

As always in this specialization, tough course but so practical !!