이 강좌에 대하여

최근 조회 41,367
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 3개 강좌 중 1번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
초급 단계

Accessible to business-side learners yet also vital to techies. Engage in the commercial use of ML – whether you're an enterprise leader or a quant.

완료하는 데 약 13시간 필요
영어
자막: 영어

귀하가 습득할 기술

Data ScienceArtificial Intelligence (AI)Machine LearningPredictive AnalyticsEthics Of Artificial Intelligence
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 3개 강좌 중 1번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
초급 단계

Accessible to business-side learners yet also vital to techies. Engage in the commercial use of ML – whether you're an enterprise leader or a quant.

완료하는 데 약 13시간 필요
영어
자막: 영어

제공자:

Placeholder

SAS

강의 계획 - 이 강좌에서 배울 내용

1

1

완료하는 데 1시간 필요

MODULE 0 - Introduction

완료하는 데 1시간 필요
9개 동영상 (총 55분), 1 개의 읽기 자료
9개의 동영상
Specialization overview: Machine Learning for Everyone4m
Why this course isn't "hands-on" & why it's still good for techies anyway8m
What you'll learn: topics covered and learning objectives3m
Vendor-neutral courses with complementary demos from SAS3m
DEMO - Exploring SAS® Visual Data Mining and Machine Learning (optional)11m
Deep learning: your path towards leveraging the hottest ML method4m
A tour of this specialization's courses4m
Your instructor: a rap star stuck in a nerd's body8m
1개의 읽기 자료
One-question survey1m
완료하는 데 4시간 필요

MODULE 1 - The Impact of Machine Learning

완료하는 데 4시간 필요
13개 동영상 (총 79분), 6 개의 읽기 자료, 15 개의 테스트
13개의 동영상
The Obama example: forecasting vs. predictive analytics4m
The full definitions of machine learning and predictive analytics5m
Buzzword heyday: putting big data and data science in their place5m
The two stages of machine learning: modeling and scoring5m
Targeting marketing with response modeling5m
The Prediction effect: A little prediction goes a long way5m
Targeted customer retention with churn modeling6m
Why targeting ads is like the movie "Groundhog Day"6m
Another application: financial credit risk7m
Myriad opportunities: the great range of application areas7m
"Non-predictive" applications: detection, classification, and diagnosis5m
Why ML is the latest evolutionary step of the Information Age4m
6개의 읽기 자료
Nate Silver on misunderstanding election forecasts (optional)10m
Predictive analytics overview25m
Detailed profit calculations for targeted marketing (optional)5m
More information about named examples (optional) 5m
Predictive analytics applications (optional)5m
White paper overviewing the organizational value of predictive analytics15m
15개 연습문제
Predicting the president: two common misconceptions about forecasting2m
The Obama example: forecasting vs. predictive analytics2m
The full definitions of machine learning and predictive analytics2m
Buzzword heyday: putting big data and data science in their place2m
The two stages of machine learning: modeling and scoring4m
Targeting marketing with response modeling4m
The Prediction effect: A little prediction goes a long way2m
Targeted customer retention with churn modeling4m
Why targeting ads is like the movie "Groundhog Day"2m
Another application: financial credit risk2m
Myriad opportunities: the great range of application areas2m
"Non-predictive" applications: detection, classification, and diagnosis2m
Why ML is the latest evolutionary step of the Information Age2m
A question about the reading – the organizational value of predictive analytics2m
Module 1 Review 30m
2

2

완료하는 데 2시간 필요

MODULE 2 - Data: the New Oil

완료하는 데 2시간 필요
11개 동영상 (총 63분), 1 개의 읽기 자료, 11 개의 테스트
11개의 동영상
A paradigm shift for scientific discovery: its automation5m
Example discoveries from data6m
The Data Effect: Data is always predictive4m
Training data -- what it looks like6m
Predicting with one single variable4m
Growing a decision tree to combine variables6m
More on decision trees5m
The light bulb puzzle4m
Measuring predictive performance: lift6m
DEMO - Training a simple decision tree model (optional)9m
1개의 읽기 자료
How spending habits reveal debtor reliability (optional)5m
11개 연습문제
The big deal about big data2m
A paradigm shift for scientific discovery: its automation2m
Example discoveries from data2m
The Data Effect: Data is always predictive2m
Training data -- what it looks like4m
Predicting with one single variable2m
Growing a decision tree to combine variables2m
More on decision trees2m
The light bulb puzzle4m
Measuring predictive performance: lift2m
Module 2 Review30m
3

3

완료하는 데 3시간 필요

MODULE 3 - Predictive Models: What Gets Learned from Data

완료하는 데 3시간 필요
11개 동영상 (총 70분), 4 개의 읽기 자료, 11 개의 테스트
11개의 동영상
How can you trust a predictive model (train/test)?5m
More predictive modeling principles 6m
Visually comparing modeling methods - decision boundaries5m
DEMO - Training and comparing multiple models (optional)8m
Deploying a predictive model8m
The profit curve of a model7m
Deployment results in targeting marketing and sales6m
Deep learning - application areas and limitations6m
Labeled data: a source of great power, yet a major limitation5m
Talking computers -- natural language processing and text analytics4m
4개의 읽기 자료
Prescriptive vs. Predictive Analytics – A Distinction without a Difference (optional)5m
Predictive analytics deployment and profit (optional)5m
More on deep learning (optional)15m
The difference between Watson and Siri (optional) 5m
11개 연습문제
The principles of predictive modeling3m
How can you trust a predictive model (train/test)?2m
More predictive modeling principles 2m
Visually comparing modeling methods - decision boundaries2m
Deploying a predictive model2m
The profit curve of a model2m
Deployment results in targeting marketing and sales2m
Deep learning - application areas and limitations2m
Labeled data: a source of great power, yet a major limitation2m
Talking computers – natural language processing and text analytics2m
Module 3 Review30m
4

4

완료하는 데 3시간 필요

MODULE 4 - Industry Perspective: AI Myths and Real Ethical Risks

완료하는 데 3시간 필요
10개 동영상 (총 70분), 4 개의 읽기 자료, 10 개의 테스트
10개의 동영상
Dismantling the logical fallacy that is AI6m
Why legitimizing AI as a field incurs great cost6m
Ethics overview: five ways ML threatens social justice9m
Blatantly discriminatory models7m
The trend towards discriminatory models6m
The argument against discriminatory models7m
Five myths about "evil" big data8m
Defending machine learning -- how it does good6m
Course wrap-up3m
4개의 읽기 자료
AI is a big fat lie (optional) 10m
AI is an ideology, not a technology (optional)10m
Book Review: Weapons of Math Destruction by Cathy O'Neil15m
Coded gaze on speech recognition (optional)5m
10개 연습문제
Why machine learning isn't becoming superintelligent2m
Dismantling the logical fallacy that is AI2m
Why legitimizing AI as a field incurs great cost2m
Ethics overview: five ways ML threatens social justice2m
Blatantly discriminatory models4m
The trend towards discriminatory models2m
The argument against discriminatory models8m
Five myths about "evil" big data5m
Defending machine learning -- how it does good2m
Module 4 Review 30m

검토

THE POWER OF MACHINE LEARNING: BOOST BUSINESS, ACCUMULATE CLICKS, FIGHT FRAUD, AND DENY DEADBEATS의 최상위 리뷰

모든 리뷰 보기

Machine Learning for Everyone with Eric Siegel 특화 과정 정보

Machine learning reinvents industries and runs the world. Harvard Business Review calls it “the most important general-purpose technology of our era.” But while there are many how-to courses for hands-on techies, there are practically none that also serve business leaders – a striking omission, since success with machine learning relies on a very particular business leadership practice just as much as it relies on adept number crunching. This specialization fills that gap. It empowers you to generate value with ML by ramping you up on both the tech and business sides – both the cutting edge algorithms and the project management skills needed for successful deployment. NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this specialization serves both business leaders and burgeoning data scientists with expansive, holistic coverage. BUT TECHNICAL LEARNERS SHOULD TAKE ANOTHER LOOK. Before jumping straight into the hands-on, as quants are inclined to do, consider one thing: This curriculum provides complementary know-how that all great techies also need to master. It guides you on the end-to-end process required to successfully deploy ML so that it delivers a business impact. WHAT YOU'LL LEARN. How ML works, how to report on its ROI and predictive performance, best practices to lead an ML project, technical tips and tricks, how to avoid the major pitfalls, whether true AI is coming or is just a myth, and the risks to social justice that stem from ML....
Machine Learning for Everyone with Eric Siegel

자주 묻는 질문

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.