About this Course

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Beginner Level

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.

Approx. 13 hours to complete
English

Skills you will gain

Data ScienceArtificial Intelligence (AI)Machine LearningPredictive AnalyticsEthics Of Artificial Intelligence
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level

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.

Approx. 13 hours to complete
English

Offered by

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SAS

Syllabus - What you will learn from this course

Week
1

Week 1

1 hour to complete

MODULE 0 - Introduction

1 hour to complete
9 videos (Total 55 min), 1 reading
9 videos
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
1 reading
One-question survey1m
4 hours to complete

MODULE 1 - The Impact of Machine Learning

4 hours to complete
13 videos (Total 79 min), 6 readings, 15 quizzes
13 videos
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
6 readings
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
15 practice exercises
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
Week
2

Week 2

2 hours to complete

MODULE 2 - Data: the New Oil

2 hours to complete
11 videos (Total 63 min), 1 reading, 11 quizzes
11 videos
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
1 reading
How spending habits reveal debtor reliability (optional)5m
11 practice exercises
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
Week
3

Week 3

3 hours to complete

MODULE 3 - Predictive Models: What Gets Learned from Data

3 hours to complete
11 videos (Total 70 min), 4 readings, 11 quizzes
11 videos
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
4 readings
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
11 practice exercises
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
Week
4

Week 4

3 hours to complete

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

3 hours to complete
10 videos (Total 70 min), 4 readings, 10 quizzes
10 videos
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
4 readings
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
10 practice exercises
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

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About the Machine Learning for Everyone with Eric Siegel Specialization

Machine Learning for Everyone with Eric Siegel

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