课程信息

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初级

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.

完成时间大约为13 小时
英语(English)

您将学到的内容有

  • 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”

您将获得的技能

Data ScienceArtificial Intelligence (AI)Machine LearningPredictive AnalyticsEthics Of Artificial Intelligence
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
初级

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.

完成时间大约为13 小时
英语(English)

提供方

Placeholder

SAS

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 1 小时

MODULE 0 - Introduction

完成时间为 1 小时
9 个视频 (总计 54 分钟), 2 个阅读材料
9 个视频
Specialization overview: Machine Learning for Everyone4分钟
Why this course isn't "hands-on" & why it's still good for techies anyway8分钟
What you'll learn: topics covered and learning objectives3分钟
Vendor-neutral courses with complementary demos from SAS3分钟
DEMO - Exploring SAS® Visual Data Mining and Machine Learning (optional)11分钟
Deep learning: your path towards leveraging the hottest ML method4分钟
A tour of this specialization's courses4分钟
About your instructor, Eric Siegel7分钟
2 个阅读材料
The Machine Learning Glossary (optional)10分钟
One-question survey1分钟
完成时间为 4 小时

MODULE 1 - The Impact of Machine Learning

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

2

完成时间为 2 小时

MODULE 2 - Data: the New Oil

完成时间为 2 小时
11 个视频 (总计 63 分钟), 1 个阅读材料, 11 个测验
11 个视频
A paradigm shift for scientific discovery: its automation5分钟
Example discoveries from data6分钟
The Data Effect: Data is always predictive4分钟
Training data -- what it looks like6分钟
Predicting with one single variable4分钟
Growing a decision tree to combine variables6分钟
More on decision trees5分钟
The light bulb puzzle4分钟
Measuring predictive performance: lift6分钟
DEMO - Training a simple decision tree model (optional)9分钟
1 个阅读材料
How spending habits reveal debtor reliability (optional)5分钟
11 个练习
The big deal about big data2分钟
A paradigm shift for scientific discovery: its automation2分钟
Example discoveries from data2分钟
The Data Effect: Data is always predictive2分钟
Training data -- what it looks like4分钟
Predicting with one single variable2分钟
Growing a decision tree to combine variables2分钟
More on decision trees2分钟
The light bulb puzzle4分钟
Measuring predictive performance: lift2分钟
Module 2 Review30分钟
3

3

完成时间为 3 小时

MODULE 3 - Predictive Models: What Gets Learned from Data

完成时间为 3 小时
11 个视频 (总计 70 分钟), 4 个阅读材料, 11 个测验
11 个视频
How can you trust a predictive model (train/test)?5分钟
More predictive modeling principles 6分钟
Visually comparing modeling methods - decision boundaries5分钟
DEMO - Training and comparing multiple models (optional)8分钟
Deploying a predictive model8分钟
The profit curve of a model7分钟
Deployment results in targeting marketing and sales6分钟
Deep learning - application areas and limitations6分钟
Labeled data: a source of great power, yet a major limitation5分钟
Talking computers -- natural language processing and text analytics4分钟
4 个阅读材料
Prescriptive vs. Predictive Analytics – A Distinction without a Difference (optional)5分钟
Predictive analytics deployment and profit (optional)5分钟
More on deep learning (optional)15分钟
The difference between Watson and Siri (optional) 5分钟
11 个练习
The principles of predictive modeling3分钟
How can you trust a predictive model (train/test)?2分钟
More predictive modeling principles 2分钟
Visually comparing modeling methods - decision boundaries2分钟
Deploying a predictive model2分钟
The profit curve of a model2分钟
Deployment results in targeting marketing and sales2分钟
Deep learning - application areas and limitations2分钟
Labeled data: a source of great power, yet a major limitation2分钟
Talking computers – natural language processing and text analytics2分钟
Module 3 Review30分钟
4

4

完成时间为 3 小时

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

完成时间为 3 小时
10 个视频 (总计 70 分钟), 4 个阅读材料, 10 个测验
10 个视频
Dismantling the logical fallacy that is AI6分钟
Why legitimizing AI as a field incurs great cost6分钟
Ethics overview: five ways ML threatens social justice9分钟
Blatantly discriminatory models7分钟
The trend towards discriminatory models6分钟
The argument against discriminatory models7分钟
Five myths about "evil" big data8分钟
Defending machine learning -- how it does good6分钟
Course wrap-up3分钟
4 个阅读材料
AI is a big fat lie (optional) 10分钟
AI is an ideology, not a technology (optional)10分钟
Book Review: Weapons of Math Destruction by Cathy O'Neil15分钟
Coded gaze on speech recognition (optional)5分钟
10 个练习
Why machine learning isn't becoming superintelligent2分钟
Dismantling the logical fallacy that is AI2分钟
Why legitimizing AI as a field incurs great cost2分钟
Ethics overview: five ways ML threatens social justice2分钟
Blatantly discriminatory models4分钟
The trend towards discriminatory models2分钟
The argument against discriminatory models8分钟
Five myths about "evil" big data5分钟
Defending machine learning -- how it does good2分钟
Module 4 Review 30分钟

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