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初級

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.

完成時間大約為13 小時
英語(English)

您將學到的內容有

  • Participate in the deployment of machine learning

  • Identify potential machine learning deployments that will generate value for your organization

  • Report on the predictive performance of machine learning and the profit it generates

  • Understand the potential of machine learning and avoid the false promises of “artificial intelligence”

您將獲得的技能

Data ScienceArtificial Intelligence (AI)Machine LearningPredictive AnalyticsEthics Of Artificial Intelligence
可分享的證書
完成後獲得證書
100% 在線
立即開始,按照自己的計劃學習。
可靈活調整截止日期
根據您的日程表重置截止日期。
初級

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.

完成時間大約為13 小時
英語(English)

提供方

Placeholder

SAS

教學大綱 - 您將從這門課程中學到什麼

1

1

完成時間為 1 小時

MODULE 0 - Introduction

完成時間為 1 小時
9 個視頻 (總計 54 分鐘), 2 個閱讀材料
9 個視頻
Specialization overview: Machine Learning for Everyone4分鐘
Why this course isn't "hands-on" & why it's still good for techies anyway8分鐘
What you'll learn: topics covered and learning objectives3分鐘
Vendor-neutral courses with complementary demos from SAS3分鐘
DEMO - Exploring SAS® Visual Data Mining and Machine Learning (optional)11分鐘
Deep learning: your path towards leveraging the hottest ML method4分鐘
A tour of this specialization's courses4分鐘
About your instructor, Eric Siegel7分鐘
2 個閱讀材料
The Machine Learning Glossary (optional)10分鐘
One-question survey1分鐘
完成時間為 4 小時

MODULE 1 - The Impact of Machine Learning

完成時間為 4 小時
13 個視頻 (總計 79 分鐘), 6 個閱讀材料, 15 個測驗
13 個視頻
The Obama example: forecasting vs. predictive analytics4分鐘
The full definitions of machine learning and predictive analytics5分鐘
Buzzword heyday: putting big data and data science in their place5分鐘
The two stages of machine learning: modeling and scoring5分鐘
Targeting marketing with response modeling5分鐘
The Prediction effect: A little prediction goes a long way5分鐘
Targeted customer retention with churn modeling6分鐘
Why targeting ads is like the movie "Groundhog Day"6分鐘
Another application: financial credit risk7分鐘
Myriad opportunities: the great range of application areas7分鐘
"Non-predictive" applications: detection, classification, and diagnosis5分鐘
Why ML is the latest evolutionary step of the Information Age4分鐘
6 個閱讀材料
Nate Silver on misunderstanding election forecasts (optional)10分鐘
Predictive analytics overview25分鐘
Detailed profit calculations for targeted marketing (optional)5分鐘
More information about named examples (optional) 5分鐘
Predictive analytics applications (optional)5分鐘
White paper overviewing the organizational value of predictive analytics15分鐘
15 個練習
Predicting the president: two common misconceptions about forecasting2分鐘
The Obama example: forecasting vs. predictive analytics2分鐘
The full definitions of machine learning and predictive analytics2分鐘
Buzzword heyday: putting big data and data science in their place2分鐘
The two stages of machine learning: modeling and scoring4分鐘
Targeting marketing with response modeling4分鐘
The Prediction effect: A little prediction goes a long way2分鐘
Targeted customer retention with churn modeling4分鐘
Why targeting ads is like the movie "Groundhog Day"2分鐘
Another application: financial credit risk2分鐘
Myriad opportunities: the great range of application areas2分鐘
"Non-predictive" applications: detection, classification, and diagnosis2分鐘
Why ML is the latest evolutionary step of the Information Age2分鐘
A question about the reading – the organizational value of predictive analytics2分鐘
Module 1 Review 30分鐘
2

2

完成時間為 2 小時

MODULE 2 - Data: the New Oil

完成時間為 2 小時
11 個視頻 (總計 63 分鐘), 1 個閱讀材料, 11 個測驗
11 個視頻
A paradigm shift for scientific discovery: its automation5分鐘
Example discoveries from data6分鐘
The Data Effect: Data is always predictive4分鐘
Training data -- what it looks like6分鐘
Predicting with one single variable4分鐘
Growing a decision tree to combine variables6分鐘
More on decision trees5分鐘
The light bulb puzzle4分鐘
Measuring predictive performance: lift6分鐘
DEMO - Training a simple decision tree model (optional)9分鐘
1 個閱讀材料
How spending habits reveal debtor reliability (optional)5分鐘
11 個練習
The big deal about big data2分鐘
A paradigm shift for scientific discovery: its automation2分鐘
Example discoveries from data2分鐘
The Data Effect: Data is always predictive2分鐘
Training data -- what it looks like4分鐘
Predicting with one single variable2分鐘
Growing a decision tree to combine variables2分鐘
More on decision trees2分鐘
The light bulb puzzle4分鐘
Measuring predictive performance: lift2分鐘
Module 2 Review30分鐘
3

3

完成時間為 3 小時

MODULE 3 - Predictive Models: What Gets Learned from Data

完成時間為 3 小時
11 個視頻 (總計 70 分鐘), 4 個閱讀材料, 11 個測驗
11 個視頻
How can you trust a predictive model (train/test)?5分鐘
More predictive modeling principles 6分鐘
Visually comparing modeling methods - decision boundaries5分鐘
DEMO - Training and comparing multiple models (optional)8分鐘
Deploying a predictive model8分鐘
The profit curve of a model7分鐘
Deployment results in targeting marketing and sales6分鐘
Deep learning - application areas and limitations6分鐘
Labeled data: a source of great power, yet a major limitation5分鐘
Talking computers -- natural language processing and text analytics4分鐘
4 個閱讀材料
Prescriptive vs. Predictive Analytics – A Distinction without a Difference (optional)5分鐘
Predictive analytics deployment and profit (optional)5分鐘
More on deep learning (optional)15分鐘
The difference between Watson and Siri (optional) 5分鐘
11 個練習
The principles of predictive modeling3分鐘
How can you trust a predictive model (train/test)?2分鐘
More predictive modeling principles 2分鐘
Visually comparing modeling methods - decision boundaries2分鐘
Deploying a predictive model2分鐘
The profit curve of a model2分鐘
Deployment results in targeting marketing and sales2分鐘
Deep learning - application areas and limitations2分鐘
Labeled data: a source of great power, yet a major limitation2分鐘
Talking computers – natural language processing and text analytics2分鐘
Module 3 Review30分鐘
4

4

完成時間為 3 小時

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

完成時間為 3 小時
10 個視頻 (總計 70 分鐘), 4 個閱讀材料, 10 個測驗
10 個視頻
Dismantling the logical fallacy that is AI6分鐘
Why legitimizing AI as a field incurs great cost6分鐘
Ethics overview: five ways ML threatens social justice9分鐘
Blatantly discriminatory models7分鐘
The trend towards discriminatory models6分鐘
The argument against discriminatory models7分鐘
Five myths about "evil" big data8分鐘
Defending machine learning -- how it does good6分鐘
Course wrap-up3分鐘
4 個閱讀材料
AI is a big fat lie (optional) 10分鐘
AI is an ideology, not a technology (optional)10分鐘
Book Review: Weapons of Math Destruction by Cathy O'Neil15分鐘
Coded gaze on speech recognition (optional)5分鐘
10 個練習
Why machine learning isn't becoming superintelligent2分鐘
Dismantling the logical fallacy that is AI2分鐘
Why legitimizing AI as a field incurs great cost2分鐘
Ethics overview: five ways ML threatens social justice2分鐘
Blatantly discriminatory models4分鐘
The trend towards discriminatory models2分鐘
The argument against discriminatory models8分鐘
Five myths about "evil" big data5分鐘
Defending machine learning -- how it does good2分鐘
Module 4 Review 30分鐘

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