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Machine Learning: Classification に戻る

ワシントン大学(University of Washington) による Machine Learning: Classification の受講者のレビューおよびフィードバック

4.7
2,827件の評価
473件のレビュー

コースについて

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)....

人気のレビュー

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

フィルター:

Machine Learning: Classification: 426 - 441 / 441 レビュー

by Keith L

Nov 25, 2016

Not as polished/comprehensive as the previous courses (especially week1, week5 and week6). But useful techniques nevertheless.

by Yaron K

Sep 30, 2016

The assignments are well thought out and explain the algorithms step-by-step. The subtitles/transcripts are a disappointment :( . Full of mistakes. Sometimes to the point of being useless or even worse - saying the exact of opposite of what the lecturer says. Since the lecturer sometimes is unclear - this is problematic. As usual - Graphlab Create sometimes crashes, however there are explanations how to run the assignments using Scikit-Learn.

by Vasilios D

Oct 05, 2016

I am afraid that this course is, to a large extend, a marketing tool for promoting the instructors' proprietary product. Its use is therefore limited for the practitioners that want a foundation on the free Python data/ML capabilities.

I would not recommend this course to my colleagues.

by Stefan W

Oct 28, 2018

The speaker is very difficult to understand, and the environment for writing code is awful (web browser).

by Gaurav B

Jul 04, 2019

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

by Adrien L

Feb 02, 2017

No good without the missing course and capstone projects

by Grzegorz N

Mar 15, 2016

After 2 great courses this one is really disappointing!

by Eugene K

Feb 10, 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.

by Ernie M

Sep 25, 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.

by Ken C

Feb 04, 2017

Not happy about course 5 & 6 got cancelled.

by Simen K R

Jul 13, 2017

Poor quality.

by Yukai Z

Jun 03, 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.

by Charles G

Aug 12, 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.

by Ehsan M

Mar 22, 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

by Andreas

Jan 04, 2017

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

by NIKHIL K S

Sep 07, 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