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Opiniones y comentarios de aprendices correspondientes a Machine Learning: Classification por parte de Universidad de Washington

3,665 calificaciones
603 reseña

Acerca del Curso

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)....

Principales reseñas


14 de jun. de 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 :)


15 de oct. de 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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151 - 175 de 574 revisiones para Machine Learning: Classification

por Dmitri T

25 de abr. de 2016

Really liked the practical application of this course - very useful in learning classification methods.

por Deepak S

21 de ago. de 2020

Assignments are great providing an opportunity to have better understanding about the topic discussed


3 de may. de 2019

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

por Jonathan C

19 de ene. de 2018

wow this was a good course. things got real here and hard. but I feel like I can do anything now

por Yuexiu C

20 de ene. de 2017

The instructor is awesome. He explained the boring statistical method in a very interesting way!

por Filipe P L

2 de oct. de 2016

Very good, sometimes is a little hard, but is very helpful and have a lot of practical exercises

por Evgeni S

10 de jun. de 2016

Very focused overview of different classification methods. Goes deeper than in other ML classes.

por Patrick M

8 de ago. de 2016

Excellent course. Great mix of theory overview coupled with practical examples to work through.

por Ayush K G

1 de nov. de 2017

Usefull for getting ideas and depth knowledge in Classification. Explained in very simple way.

por Arslan a

18 de feb. de 2019

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

por Evaldas B

14 de dic. de 2017

Very nice course with a little bit of details about how classification is done. Enjoyed it.

por Aakash S

14 de jun. de 2019

Amazing Explanation of every thing related to Classification.

Thanks a lot for the course.

por Viktor K

14 de may. de 2021

I m learn many things in the coursera. This is one of the best app provide for everyone.

por Gustavo d A C

23 de abr. de 2017

It was a nice course. I could learn many new techniques and algorithms. Very exciting !!

por Mounika G

3 de may. de 2020

I have learnt many things from these course .This course helped me to learn from online

por Rahul M

12 de nov. de 2017

awesome course material to nourish your brain to classify in better decision making...

por Kim K L

13 de ago. de 2016

Another classic and fantastic. Love this Course and learn so much. Highly recommended!

por Patrick A

27 de jun. de 2020

As usual, very simple way of explaining principles. Thanks very much for this course!

por andreas c c

16 de ago. de 2017

The course is demanding but I learn a lot in classification.

The teachers are awesome!

por Simon C

28 de oct. de 2016

Great content and exercises which facilitated understanding of very complex concepts.

por Jifu Z

22 de jul. de 2016

Good class, But it would be much better if the quiz is open to those who doesn't pay.

por Sanjay M

30 de jun. de 2017

Very nice course with good mix of machine learning concepts with maths, programming.

por Suraj P

19 de jul. de 2021

Nice Course for detail understanding of machine learning classification algorithms.

por Saheed S

18 de jul. de 2017

It was a great course, I will start working on a new classification project. Thanks

por Darryl L

27 de oct. de 2016

they do a good job explaining concepts in great detail so everyone can learn it.