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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

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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


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.


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

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 3,043개 리뷰 중 2701~2725

교육 기관: Jijo T

2015년 10월 6일

I love the hands on exercises.

교육 기관: Mazen A

2016년 10월 9일

the best introduction for ML.

교육 기관: Rishabh C

2020년 7월 23일

Awesome course to start with

교육 기관: Rakesh G

2019년 4월 15일

A good beginners guide to ML

교육 기관: RISHAB P H

2020년 4월 15일

add more practical's please

교육 기관: Mahesh B

2019년 10월 10일

Good start for ML beginners

교육 기관: Poornima S

2019년 2월 18일

It is designed really good.

교육 기관: Hyeong R J

2017년 2월 2일

Good lecture and practices.

교육 기관: Marcos M M

2017년 8월 24일

Great introductory course!

교육 기관: GABRIEL O C D O

2021년 4월 15일

The course needs updating

교육 기관: SUPRIYA V S

2018년 6월 30일

Nice course for beginners

교육 기관: Vinicius G d O

2016년 6월 23일

Good introductory course.

교육 기관: José T G R

2015년 11월 1일

Very good!!! Excellent!!!

교육 기관: Tushar A

2020년 7월 13일

This is a nice course..

교육 기관: Fernando S

2017년 8월 20일

Easy going, very good!!

교육 기관: Godwin

2017년 6월 4일

Very interesting :) WOW

교육 기관: Annie I R

2016년 1월 4일

This is a great course.

교육 기관: Mayur S

2017년 1월 18일

its good, if new to ML

교육 기관: Shikhar S

2020년 12월 8일

Great course to start

교육 기관: Wridheeman B

2020년 6월 30일

It was a great course

교육 기관: Eric S

2016년 1월 5일

Pretty good, overall.

교육 기관: Mahajan P J

2019년 12월 26일

The course was good.

교육 기관: Richik G

2019년 7월 11일

computer vision best

교육 기관: Pieterjan C

2017년 10월 2일

very useful to start

교육 기관: Shreeti S

2017년 8월 16일

Good to start with.