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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
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배울 내용

  • Apply ML: Identify opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and more

  • Plan ML: Determine the way machine learning will be operationally integrated and deployed, and the staffing and data requirements to get there

  • Greenlight ML: Forecast the effectiveness of a machine learning project and then internally sell it, gaining buy-in from your colleagues

  • Lead ML: Manage a machine learning project, from the generation of predictive models to their launch

귀하가 습득할 기술

Data ScienceArtificial Intelligence (AI)Machine LearningMachine learning strategy and leadershipPredictive Analytics
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 3개 강좌 중 2번째 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

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강의 계획 - 이 강좌에서 배울 내용

1

1

완료하는 데 3시간 필요

MODULE 1 - Business Applications of Machine Learning

완료하는 데 3시간 필요
13개 동영상 (총 92분), 4 개의 읽기 자료, 14 개의 테스트
13개의 동영상
The ingredients of a machine learning application4m
Risky business: predictive analytics enacts risk management10m
Response modeling to target marketing6m
Gains curves for response modeling 7m
Churn modeling to target customer retention6m
Case study: targeting ads9m
Case study: product recommendations8m
Credit scoring6m
Five ways insurance companies use machine learning8m
Fraud detection6m
Case study: insurance fraud detection6m
Machine learning for government and healthcare5m
4개의 읽기 자료
One-question survey1m
Retaining new customers, a killer app similar to churn modeling (optional)10m
More information about named examples (optional) 5m
Generating compelling text with deep learning (optional)10m
14개 연습문제
Course overview2m
The ingredients of a machine learning application2m
Risky business: predictive analytics enacts risk management2m
Response modeling to target marketing2m
Gains curves for response modeling 2m
Churn modeling to target customer retention2m
Case study: targeting ads2m
Case study: product recommendations2m
Credit scoring2m
Five ways insurance companies use machine learning2m
Fraud detection2m
Case study: insurance fraud detection2m
Machine learning for government and healthcare2m
Module 1 Review30m
2

2

완료하는 데 3시간 필요

MODULE 2 - Scoping, Greenlighting, and Managing Machine Learning Initiatives

완료하는 데 3시간 필요
12개 동영상 (총 67분), 7 개의 읽기 자료, 12 개의 테스트
12개의 동영상
The six steps for running a ML project4m
Running and iterating on the process steps7m
How long a machine learning project takes2m
Refining the prediction goal5m
Where to start -- picking your first ML project5m
Strategic objectives and key performance indicators9m
Personnel - staffing your machine learning team6m
Sourcing the staff for a machine learning project4m
Greenlighting: Internally selling a machine learning initiative5m
More tips for getting the green light6m
The most important video about ML ever, period2m
7개의 읽기 자료
ML project management pitfalls and best practices (optional)10m
Choosing the right analytics problem (optional)10m
Six ways to lower costs with predictive analytics (optional)10m
Counterpoint: AI success comes through growth, not labor savings (optional)10m
Top 10 roles in AI and data science (optional)10m
The analytics engineer (optional)10m
Need a data scientist? Try building a "DataScienceStein" (optional)10m
12개 연습문제
Project management overview2m
The six steps for running a ML project2m
Running and iterating on the process steps4m
How long a machine learning project takes2m
Refining the prediction goal2m
Where to start -- picking your first ML project2m
Strategic objectives and key performance indicators4m
Personnel - staffing your machine learning team2m
Sourcing the staff for a machine learning project2m
Greenlighting: Internally selling a machine learning initiative2m
More tips for getting the green light2m
Module 2 Review30m
3

3

완료하는 데 3시간 필요

MODULE 3 - Data Prep: Preparing the Training Data

완료하는 데 3시간 필요
14개 동영상 (총 110분), 2 개의 읽기 자료, 15 개의 테스트
14개의 동영상
Defining the dependent variable8m
Refining the predictive goal statement in detail7m
Identifying the sub-problem8m
How much data do you need, and how balanced?9m
A flash from the past: independent variables6m
Behavioral versus demographic data9m
Derived variables8m
Five colorful examples of behavioral data for workforce analytics 6m
The predictive value of social media data9m
More social data: population trends and interpreting sentiment4m
Merging in other sources of data7m
Data cleansing: what kind of noise is okay?8m
Data disaster: "High school dropouts are better hires"5m
2개의 읽기 자료
It is a mistake to ask the wrong question (optional)10m
It is a mistake to accept leaks from the future (optional)10m
15개 연습문제
Data prep for-the-win -- why it's absolutely crucial2m
Defining the dependent variable2m
Refining the predictive goal statement in detail2m
Identifying the sub-problem2m
How much data do you need, and how balanced?4m
A flash from the past: independent variables4m
Behavioral versus demographic data2m
Derived variables2m
Five colorful examples of behavioral data for workforce analytics 2m
The predictive value of social media data2m
More social data: population trends and interpreting sentiment2m
Merging in other sources of data2m
Data cleansing: what kind of noise is okay?4m
Data disaster: "High school dropouts are better hires"2m
Module 3 Review30m
4

4

완료하는 데 3시간 필요

MODULE 4 - The High Cost of False Promises, False Positives, and Misapplied Models

완료하는 데 3시간 필요
9개 동영상 (총 66분), 4 개의 읽기 자료, 10 개의 테스트
9개의 동영상
More accuracy fallacies: predicting psychosis, criminality, & bestsellers9m
The cost of false positives and false negatives6m
Assigning costs: so important, yet so difficult4m
Machine learning for social good7m
Predicting pregnancy -- and other sensitive machine inductions9m
Predatory micro-targeting6m
Predictive policing in law enforcement and national security10m
Course wrap-up2m
4개의 읽기 자료
More reading related to the accuracy fallacy (optional)1m
Machine learning for social good - more examples (optional)10m
Further insights on predicting sensitive attributes (optional)10m
Further analyses of predictive policing and ML’s effect on the balance of power (optional)10m
10개 연습문제
Accuracy fallacy: orchestrating the media's bogus coverage of ML2m
More accuracy fallacies: predicting psychosis, criminality, & bestsellers2m
The cost of false positives and false negatives2m
Assigning costs: so important, yet so difficult2m
Machine learning for social good2m
Predicting pregnancy -- and other sensitive machine inductions2m
Predatory micro-targeting2m
Predictive policing in law enforcement and national security2m
Course wrap-up2m
Module 4 Review30m

검토

LAUNCHING MACHINE LEARNING: DELIVERING OPERATIONAL SUCCESS WITH GOLD STANDARD ML LEADERSHIP의 최상위 리뷰

모든 리뷰 보기

Machine Learning for Everyone with Eric Siegel 특화 과정 정보

Machine Learning for Everyone with Eric Siegel

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