This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project.
제공자:
Machine Learning: Algorithms in the Real World 특화 과정Alberta Machine Intelligence Institute이 강좌에 대하여
최근 조회 21,669회
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 1번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 7시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 중국어 (간체자), 이탈리아어, 베트남어, 한국어, 독일어, 러시아어, 터키어, 영어, 스페인어
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 1번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 7시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 중국어 (간체자), 이탈리아어, 베트남어, 한국어, 독일어, 러시아어, 터키어, 영어, 스페인어
제공자:
Alberta Machine Intelligence Institute
The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 3시간 필요
Introduction to Machine Learning Applications
This week, you will learn about what machine learning (ML) actually is, contrast different problem scenarios, and explore some common misconceptions about ML. You will apply this knowledge by identifying different components essential to a machine learning business solution.
완료하는 데 3시간 필요
12개 동영상 (총 44분), 6 개의 읽기 자료, 2 개의 테스트
12개의 동영상
Instructor Introduction1m
Introduction to Course 12m
What is Artificial Intelligence and Machine Learning?5m
What about Data Science?3m
The Machine Learning Process4m
The Three Kinds of Machine Learning3m
Classification: What is it and how does it work?3m
Regression: Fitting lines and predicting numbers3m
Unsupervised Learning4m
Reinforcement Learning6m
Weekly Summary1m
6개의 읽기 자료
What about Deep Learning? (supplemental)10m
Fooling Neural Networks (supplemental)10m
How to Curate A Ground Truth For Your Business Dataset (Required)10m
Learning From Multiple Annotators: A Survey (supplemental)10m
Inferring the Ground Truth Through Crowdsourcing (supplemental)10m
Semi Supervised Learning (required)10m
2개 연습문제
Concepts and Definitions20m
Identifying Machine Learning Techniques10m
완료하는 데 2시간 필요
Machine Learning in the Real World
This week, you will learn how to translate a business need into a machine learning problem. We'll walk through some applied examples so you can get a feel for what makes a well-defined question for your QuAM. Narrowing down your question and making sure you have the data necessary to learn is critical to ML success!
완료하는 데 2시간 필요
8개 동영상 (총 34분), 4 개의 읽기 자료, 1 개의 테스트
8개의 동영상
Features and transformations of raw data6m
Farmer Betty and Her Precision Agriculture Plans3m
What to consider when using your QuAM2m
Broad Examples Narrowed Down4m
Identify Business Evaluation4m
Everything is a Proxy4m
Weekly Summary2m
4개의 읽기 자료
A Brief Introduction into Precision Agriculture10m
Farmer Betty Tried Unsupervised Learning (required)10m
Data is Central to Your ML Problem (required)10m
Martin Zinkevich's Rules for ML (supplemental)10m
1개 연습문제
Machine Learning in the Real World Review
완료하는 데 1시간 필요
Learning Data
This week is all about data. You will learn about data acquisition and understand the various sources of training data. We'll talk about how much data you need and what pitfalls might arise, including ethical issues.
완료하는 데 1시간 필요
9개 동영상 (총 34분), 2 개의 읽기 자료, 1 개의 테스트
9개의 동영상
How Much Data Do I Need?4m
Ethical Issues4m
Bias in Data Sources3m
Noise and Sources of Randomness5m
Image Classification Example3m
Data Cleaning: Everybody's favourite task4m
Why you need to set up a Data Pipeline4m
Weekly Summary1m
2개의 읽기 자료
Data Protection Laws (required)10m
Government readings on data privacy (supplemental)10m
1개 연습문제
Understanding Data for ML
완료하는 데 1시간 필요
Machine Learning Projects
This week you will learn about the Machine Learning Process Lifecycle (MLPL). After understanding the definitions and components of the MLPL you will analyze the application of the MLPL on a case study.
완료하는 데 1시간 필요
7개 동영상 (총 35분), 2 개의 읽기 자료, 1 개의 테스트
7개의 동영상
MLPL as experienced by Farmer Betty3m
Exploring the process of problem definition7m
Assessing your QuAM for use in your Business6m
Technically Assessing the Strength of your QuAM6m
Different Kinds of Wrong4m
Weekly Summary2m
2개의 읽기 자료
Machine Learning Process Lifecycle Explained10m
Deep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)10m
1개 연습문제
Understanding Machine Learning Projects
검토
INTRODUCTION TO APPLIED MACHINE LEARNING의 최상위 리뷰
SR 제공2020년 10월 8일
I have been teaching Machine Learning for some time now. This course is really good and comprehensively presents the working of machine learning process in a nutshell. Very good introductory course.
MM 제공2019년 10월 28일
I have really got benefit from this course as a beginner to ML, it gives me the best understanding of ML. I m looking forward to getting into it more efficiently with more practices.
BW 제공2020년 11월 12일
An excellent introduction to the mechanics of ML. Not so deep that coding is required, but simultaneously not so high-level as to be abstract. A very nice intro - thanks for this!
HL 제공2020년 6월 21일
An excellent introduction to the fascinating world of machine learning and its endless applications. Loved the emphasis on the evaluation of the business prospect of ML as well.
Machine Learning: Algorithms in the Real World 특화 과정 정보
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application.
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