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Machine Learning Foundations: A Case Study Approach(으)로 돌아가기

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

4.6
별점
9,938개의 평가
2,381개의 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

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.

SZ

Dec 20, 2016

Great course!\n\nEmily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 2,303개 리뷰 중 51~75

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: Xing W

Jul 03, 2016

I was expecting to solve problems using more open-sourced package. Unfortunately, I feel this series of courses are more of an advertisement for the instructor's software company.

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: Dmitri K

May 27, 2016

The whole course based on some proprietary software. In general, it seems that the main goal of the course is promote that software.

교육 기관: sravan

Oct 13, 2016

there is no proper documentation.

at least there should be some clear instructions for first program

교육 기관: Mario L

Nov 24, 2015

I dont like the tools they used, it seems like a promotion for their company.

교육 기관: Lester L

Mar 21, 2020

Not made for Windows, you need a linux or mac VM to apply this course.

교육 기관: Tim B

Jun 04, 2019

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

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: Nitin K

Sep 12, 2019

Not good support to learning process.

교육 기관: Rohit

Apr 19, 2020

This course is pretty good for beginners. All domains are explained briefly as an introduction. The best part about this course is very good hands-on sessions which are really helpful to understand concepts. The course is not very detailed but it's very good to start with. Looking forward to quality courses ahead in this specialization.

교육 기관: Shibhikkiran D

Apr 13, 2019

This is course is very informative for a beginner. It helps you to get up and running quick provided you have little basics on Python. You should( sideline on your own interest) also pickup Statistics/Math concepts along each module to make a rewarding experience as you progress through this course.

교육 기관: Diogo J A P

Feb 15, 2016

With a funny and welcoming look and feel, this course introduces machine learning through a hands-on approach, that enables the student to properly understand what ML is all about. Very nicely done!

교육 기관: Karthik M

Dec 27, 2018

A good course to understand the basics of Machine Learning. The only issue is the use of Graphlab library. Since it only works on Python 2.7, it is not convenient for people who prefer Python 3

교육 기관: Alexandru B

Jan 21, 2016

Great course. Very informative and inspirational. I got tons of ideas from it! Thank you

교육 기관: Mallikarjuna R V

Jan 17, 2019

Wonderful opportunity to learn and execute hands on coding of Machine Learning. The amazing task that Machine Learning methods and algorithms does behind scene is understood for the following cases / intelligent applications:

1. Regression (e.g. Predicting House Price etc.)

2. Classification (e.g. Product review sentiment, Spam detection, Medical diagnosis etc.)

3. Clustering and Similarity (e.g. Grouping news articles)

4. Recommender (e.g. Amazon personalized product recommendations, Netflix personalized Movie recommendations etc.)

5. Deep Learning and Deep Features (e.g. Google image search, Image-based filtering etc.)

The main challenge for me was to code using “Python3, Pandas and SciKit-Learn” instead of “Python2, GraphLab Create and SFrame”. I am now confident to develop intelligent applications based on Machine Learning. Thanks to Professors (Emily and Carlos) and to Ashok Leyland-HR for giving me this opportunity.

교육 기관: akashkr1498

Jan 18, 2019

lacture was good but one point i want to share to you don't use rare tools for assignment personally i faced lots of problem while installing graphlab better to switch to some common tools like sklearn python platform .

교육 기관: Yuvraj S

Feb 01, 2019

It is a good course if we take into account the foundational part. But since only one library has been used to solve the issues, one does not explore and write their own functions.

교육 기관: Nik M N N G

Feb 11, 2020

The material in this course severely needs an update. Some of the code examples (not from the video, because the video is obviously from old materials) are problematic. It's an interesting experience to learn a new library but I wish the experience is different. The quiz should be tougher in my honest opinion.

교육 기관: Jaime R

Dec 17, 2018

Great introduction course. However, getting the notebooks to work with Graphlab is a real pain. The notebook exercises are also mostly make-work rather than real explorations. The explanations and the notebooks themselves are pretty good though

교육 기관: Jefferson N

Feb 13, 2019

A good course, but the tools are a bit dated and it's showing its age.

교육 기관: Malik M W

Mar 31, 2020

I think this course is outdated as they are using python 2 also the platform they use for machine learning only supported by python 2. Due to these limitations, I too was unable to continue this course. Because every time you have to work with new libraries you have to uninstall python 2 and reinstall python 3.

교육 기관: Vakkalagadda A r

Dec 28, 2015

Instructors or TAs are not available in discussion forums. and the course is focussed on promoting "graph lab" proprietary package of the course sponsor. Maybe you can have a look, not beneficial if you are serious about learning ML.