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워싱턴 대학교의 Machine Learning Foundations: A Case Study Approach 학습자 리뷰 및 피드백

4.6
9,102개의 평가
2,172개의 리뷰

강좌 소개

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python....

최상위 리뷰

BL

Oct 17, 2016

Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much

PM

Aug 19, 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.\n\nThe forums and discussions were really useful and helpful while doing the assignments.

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 2,091개 리뷰 중 2076~2091

교육 기관: Krupesh A

Feb 15, 2019

Uses very old versions of libraries. Many students are facing issues which remains unsolved. Not recommended to pursue it.

교육 기관: Kaushik M

May 01, 2016

Too many videos and not cluttered assignment codes

교육 기관: Eduardo R R

Sep 23, 2015

This course rely on commercial library. I am sorry, I don't believe the convenience of a commercial library is good for your learning. You may end up locked in.

교육 기관: Phillip B

Sep 25, 2015

Would have greatly preferred if open source tools were used.

교육 기관: Chandrakant M

Sep 06, 2016

I felt that I paid for demo of the Dato/Turi.

교육 기관: Jitendra S

Apr 29, 2016

Dato tool does not even install properly.. so n´makes no sense to continue with the course. The support team fail to help in installing ... :-(

교육 기관: Darren R

Oct 13, 2015

Thoroughly disappointed to see this course based on

교육 기관: Jonathan W

May 31, 2019

The course includes some good, basic, information on machine learning. The instructors seem to know the material well. However, the exercises and coding are based on a python package written by one of the authors that, while free to students, does not easily translate into common packages such as Numpy, Scipy, Scikit-learn, Theano, TensorFlow, Keras, PyTorch, and Pandas. Also, the package used only works in Python 2 (which will no longer be supported as of January 2020).

교육 기관: Tim B

Jun 04, 2019

Complete waste of time until it is written using open-source packages.

교육 기관: Toma K

Jun 11, 2019

Warning! I paid for the specialization and now it tells me that the course ended 2 months ago!! i can't complete quizes which is why i paid!!! no options available to contact support.... no refund available....

교육 기관: Pablo S

Jul 22, 2019

I should have read the negative reviews before wasting two full days trying, and failing, to install the required software. I urge anyone reading this to avoid this course and look for alternatives.

교육 기관: Natalia Q C

Jul 25, 2019

The instructions to download GraphLab don't work and even when you sign to use the AWS platform the instructions are also old and I haven't been able to start any of the assignments because of that! I want MY MONEY BACK!!!

교육 기관: Nils W

Sep 19, 2019

The course could be great, if it won´t depend ob Python 2.7 and graphlabs (because scikit isn´t scalable). Also some quiz questions are so hard, that it is impossible to answer only with the material. So they use forum posts to answer how you can find a solution to the quizzez. So in total more a waste of time.

교육 기관: Youngmin C

Sep 06, 2019

Too old, bad packages, not much to learn. too basic.

교육 기관: Nitin K

Sep 12, 2019

Not good support to learning process.

교육 기관: Ivo R

Nov 22, 2019

This course is very frustrating because it uses a library called Turi Create that can't be installed on Windows 10. There is no support on how to setup you local environment after three days of frustration I decided to cancel my subscription.

When I opened the forum for week one all the threads were asking the same question: "How to install Turi Create on Windows 10."

It would have been much better if the course was done with a more popular library like Skit-learn.

This course is useless if you don't use a Linux or a Mac