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

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597 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

SM
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 :)

SS
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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101 - 125 de 566 revisiones para Machine Learning: Classification

por Bharat J

19 de ene. de 2018

I wish we had 5th course too,All courses are well organized and can be completed with other tool.

Hope they also include SVM and start courses on deep learning

por Ganesan P

6 de feb. de 2017

A very good course - understood a lot about classification and the understanding gained will help in reading text books like Ian Good Fellow for deep learning

por Alex L

7 de mar. de 2016

Great courses as usual like the previous courses in this specialization. Cater for beginners who want to gain a strong foundation and practical usages for ML.

por Babak P

28 de jun. de 2018

Great exposure that requires hand coding the algorithms. Really makes the concepts stick with a perfect combination of theory and programming mixed together.

por Farrukh N A

10 de feb. de 2017

I found carols to be the best instructor in machine learning domain, he presented the algorithms and all core machine learning concepts in really great way.

por OG

3 de ago. de 2016

A great combination between down to earth concepts and their implementations in python. Implementation of topics in plain python is what I enjoyed the most.

por Jane z

26 de ene. de 2020

The hands-on approach is excellent. Not only I learned ML / Classification, I was able to practice Python skills and statistical skills as well.

THANK YOU!

por Nikolay C

16 de mar. de 2016

Excellent course! I've learned these topics before, but many things were not clear enough. While learning this course my knowledge really improved a lot.

por Usman

13 de nov. de 2016

I think support vector machines is an important topic which is missing. Anyway, the programming assignments were terrific. I really enjoyed this course!

por Andrea C

7 de sep. de 2016

The course covers most important topics in depth and exercises are very interesting, them helps you to reason about some important theoretical concepts.

por Youssef R

23 de ago. de 2017

This is really a wonderfull course, and i recommend it to anyone who want to master some important techniques in the trending field of machine learning

por Josef H

26 de nov. de 2016

I like the detailed comparison between choosing different parameters for creating the classification model. I learn a lot of tricks for creating plots.

por Suoyuan S

21 de abr. de 2016

This course is friendly to machine learning beginners for the learning material is easy to understand as well as the assignment is easy to accomplish.

por Sara E E

29 de mar. de 2018

It is very intuitive and easy to follow.

I hope you add SVM and talk about linear/nonlinear decision boundaries in the next enhancement to the course.

por m w

23 de dic. de 2017

While I enjoyed most of the exercises, I found some of the implementations to be more puzzle solving rather than deeply understanding the algorithms.

por Gunjari B

21 de may. de 2018

An absolute marvel of a course! In depth explanation to everything, detailed and important concepts explained so much at ease with Carlos' humour!

por RAMESH K M

1 de ago. de 2016

The course has be described in a very precise manner. The instructor takes time to clearly explain the concepts and the importance of the same.

por Filipe G

2 de abr. de 2016

The best machine learning course I took online. I've taken other coursera courses, and this is the most complete, comprehensive, and well made.

por Richard L

15 de oct. de 2016

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

por Fan D

2 de feb. de 2017

This course is alright. For some reason I liked the regression course more as this one was a little to simple in terms of the practical.

por venkatpullela

17 de nov. de 2016

Course is really good. Assignments are taking too much time if you want to do the course rally fast, with questionable learning value.

por Sergio D H

22 de jul. de 2016

AWESOME COURSE!! Carlos and Emily are incredible teachers and the course contents are truly informative and well-paced for beginners.

por stephane d

20 de feb. de 2021

Really a great course!

Thanks to Emily and Carlos!

I still hope there will be more courses after the 4 courses of this specialization.

por Nitin D

18 de dic. de 2018

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

por Dongliang Z

22 de mar. de 2018

Excellent course! The teacher explained a lot of intuitions during the course. The optional part s are very interesting and helpful.