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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

13,082개의 평가
3,116개의 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


2016년 10월 16일

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


2019년 8월 18일

The course was well designed and delivered by all the trainers with the help of case study and great examples.

The forums and discussions were really useful and helpful while doing the assignments.

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 3,043개 리뷰 중 3026~3043

교육 기관: Arpit S

2020년 5월 22일

Improve the quality of quizes. Need to focus more on algorithm part.

교육 기관: Pratick B

2021년 8월 8일

I​nstallation of Sforce and turi was not shown adequately enough.

교육 기관: Mohamed M

2021년 9월 28일

import turicreate is hard to install and class based on it

교육 기관: Eunyoung C

2020년 8월 29일

This course could be better to use general python library.

교육 기관: Christian C

2021년 6월 5일

El curso es bueno pero esta completamente desactualizado

교육 기관: Sunita b l

2020년 7월 4일

Provide the good notes and video so all concept clear.

교육 기관: Melissa F

2021년 8월 2일

cannot get the tools installed to do any of the work.

교육 기관: Nguyen K D

2020년 6월 18일

Coursera Scam Auto Subcription. Free Fuckers

교육 기관: Gencho Z

2022년 7월 3일

Wors ML course I've had on Coursera so far.

교육 기관: Jeni

2020년 4월 17일

Instructional videos were unclear.

교육 기관: MD D I

2020년 6월 26일

I want to un enroll this course


2020년 6월 18일

Not a good course to study

교육 기관: Wenjun X

2022년 7월 23일

Poor version support

교육 기관: Jorge L G A

2020년 9월 23일

no esta en español

교육 기관: fuzhi z

2020년 12월 8일

Not recommend

교육 기관: Jijo J

2021년 4월 25일


교육 기관: Bhavya C

2021년 3월 18일


교육 기관: ABOORVA M S

2020년 5월 24일