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

3,613 calificaciones
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

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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551 - 566 de 566 revisiones para Machine Learning: Classification

por Enrico R

15 de may. de 2016

Course is too slow to keep focus, it's repetitive but not clear when it's really needed.

por Liliana V P G

13 de abr. de 2016

The classes are not practical, and the voice of the teacher is very monotone, boring.

por Gaurav B

4 de jul. de 2019

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

por SYED M I

16 de abr. de 2020


por Ernie M

25 de sep. de 2017

I enrolled in this specialization to learn machine learning using GraphLab Create. Half way into the specialization the creators sold Turi, GrapLab's parent company, making it non available to the general public (not even by paying) and then all the knowledge devalued. I wish I had known this and I would have enrolled on a different specialization. The creators still give you the possibility of using numpy, scikit learn and pandas but I had already done a lot with GraphLab create. The time I invested on my nights after work became a waste. I was trying to convince the company I worked for to buy licenses for GraphLab create.

Coursera should not allow folks to create courses that promote a private license course because it would make people waste their time and money if they decide to privatize the software.

Don't take this course, and if you take it then only use GraphLab create when the authors give you no other option.

Teaching style: Carlos was good, Emily is not very clear and loses focus of the topics and often rambles. She seems very knowledgeable but she lacks clarity of exposition when compared to Carlos or Andrew Ng.

por Charles G

12 de ago. de 2016

I was pretty disappointed with this course. Firstly, the course did not seem well balanced meaning that some weeks--particularly week 2--had A LOT of materials to watch and really felt like it was two weeks crammed into one, and then other weeks barely had anything.

Secondly, the exercises seemed unclear, poorly thought out and not really helpful. There were many errata that really should have been fixed in the beta iterations of this course.

Thirdly, I really would like to see more application and less discussion of implementing algorithms.

Fourthly, the "scaling" section was also a major disappointment. While it is mildly interesting to learn about stochastic gradient descent, I think it would have been more interesting to have a discussion about how classifiers work in a parallelized computing environment or actually to try one out using Spark.

Finally, given that GraphLab/Dato/Turi was just acquired by Apple, I question whether it is worthwhile to take this course as ALL the materials are taught using a library that in all likelihood will cease to exist.

por Yukai Z

3 de jun. de 2016

The videos are fine. But, It's SIMPLY TERRIBLE to force people to pay to be able to do the quizzes. There was no such a thing in the first two courses (by the way, I gave high rates for both). It is OK to pay for the verified certificate, however, disabling the functions in the course is a wrong way to earn money, because people who want to learn the course might not necessarily want the certificate, and this is unfair to them because it limits the resources available. This whole Specialization thing starts to make me feel like you guys are in urgent need of money, rather benefiting the community. Remember there are tons of free resources on the internet, and this only undermines your strengths. You will lose tons of potential fans. Stop being seemingly arrogant.

por Eugene K

10 de feb. de 2017

If you are considering this specialization I would recommend the Andrew Ng course instead and the main reason is that it isn't depend on proprietary ML framework. Despite the good lectures, the assignments don't help you develop the knowledge required for ML developer role.

Taking in consideration the permanent postponing the courses delivery, from summer 2016 to summer 2017, finally the most interesting part of the specialization was cancelled. I'm completely disappointed with the specialization learning expirience.

por Ehsan M

22 de mar. de 2018

very Vague and in efficient in transferring the knowledge. Teachers have tendency to overcomplicate very simple ideas to look more mathematically in-depth. It is not true and just causes confusion. I ended up to look only on slide and do the exercises rather than watching their videos

por Nikhil S

7 de sep. de 2018

The Course is not of the said level and is a very convenient way of promoting their software, the faulties are non responsive n the forums

por Christopher O

20 de oct. de 2020

Could not install required software (turicreate library for python) in a Windows Environment. The course should be explicit about that.

por Andreas

4 de ene. de 2017

This specialization is delayed for months now - very annoying! Don't give them money!

por Adrien L

2 de feb. de 2017

No good without the missing course and capstone projects

por Grzegorz N

15 de mar. de 2016

After 2 great courses this one is really disappointing!

por Ken C

4 de feb. de 2017

Not happy about course 5 & 6 got cancelled.

por Simen R

13 de jul. de 2017

Poor quality.