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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

9,061개의 평가
2,163개의 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


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


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 2,081개 리뷰 중 201~225

교육 기관: SongMyungjin

Oct 12, 2017


교육 기관: PiKaChu

Nov 27, 2017

good learning

교육 기관: Abhijit D

Nov 25, 2017

Excellent course presentation by Emily and Carlos - If courses are presented in this interactive manner learning will always be fun and interesting.

Always advisable to have some basics on python , data frame , machine learning(if possible) and you will go really smooth with this intermediate level course.

Course material really good for machine learning with real case studies and capstone project on deep learning was indeed the crown of the course.

교육 기관: 陈弘毅

Jan 09, 2018

the course is pretty and interesting, i was excited to learn all the material in the lecture with the guide of mentors

교육 기관: Manjun W

Jun 15, 2018

Very helpful. This is a good preparation for the advanced courses.

교육 기관: VITTE

Mar 11, 2018

Very interesting, useful, and up to date, this course gives the main ideas with clarity, and relevant applications, in a time format that is feasible for an active engineer.

교육 기관: Phil B

Jan 19, 2018

The course gives an excellent overview of the main types of algorithms in Machine Learning. The lecturers are both very clear and I like the combination of annotated lecture slides and jupyter notebooks.

My only problems with the course were with the coding/assignments sections. Because of the choice to use GraphLab, I was forced to install a virtual environment with Python 2 to be able to run the jupyter notebooks myself. I understand the choice and agree that GraphLab is a very intuitive and easy to use module, however if it is not going to be updated for Python 3 then the coding sections should be re-recorded using a different library.

교육 기관: 兰茜

Apr 27, 2018

Thank you!

교육 기관: Dame N

Oct 31, 2017

What a great course! Wonderfull...

교육 기관: Srinivas C

Apr 30, 2018

This course was really amazing. I got to learn a lot of new things. required to kick start advanced courses in ML.

교육 기관: Sam Z

Dec 20, 2016

Great course!

Emily 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.

교육 기관: sumita m

Nov 05, 2016

great course

교육 기관: Gene C

Jul 17, 2017

Great course love the practical introduction to the remaining course modules.

교육 기관: Chris L

Dec 03, 2016

Lots of fun, and a great introduction to ML. Will definitely be continuing on in the specialization

교육 기관: Danish h k

Sep 05, 2017

Awesome Learning course

교육 기관: Lyu Y

Mar 12, 2017

Professors are brilliant but assignments are not 'challenging' enough.

교육 기관: Gireesan N P

Aug 08, 2017

Amazing Course. Recommended to anyone with basics of Python. This is one which gives overall a good coverage on state of the art approaches to machine learning

교육 기관: Bharath C

Feb 13, 2017

This course gives the kick start needed to start a data science career.

교육 기관: Pappy S

Apr 12, 2017

Excellent course

교육 기관: SAI C P

Nov 05, 2016

The course touches upon all the necessary fundamentals of machine learning in the best way possible -- through case studies. One gets a hang of the ipython notebook and graphlab create environment too.

교육 기관: Rodrigo P

Dec 29, 2016

Excelente! Curso muito didático e com um conteúdo muito bem equilibrado.

교육 기관: fan c

Mar 21, 2017


교육 기관: Giuseppe S

Jan 26, 2018

Nice course ! I enjoyed it !

교육 기관: ASHISH D

May 28, 2017

A must for ML aspirants

교육 기관: Niyati M

Apr 13, 2017

Great one for people who need to implement it for websites and are not concerned about the underlying stuff.