À propos de ce cours

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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 heures pour terminer
Anglais

Ce que vous allez apprendre

  • 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”

Compétences que vous acquerrez

Data ScienceArtificial Intelligence (AI)Machine LearningPredictive AnalyticsEthics Of Artificial Intelligence
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau débutant

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 heures pour terminer
Anglais

Offert par

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SAS

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1

Semaine 1

1 heure pour terminer

MODULE 0 - Introduction

1 heure pour terminer
9 vidéos (Total 54 min), 2 lectures
9 vidéos
Specialization overview: Machine Learning for Everyone4 min
Why this course isn't "hands-on" & why it's still good for techies anyway8 min
What you'll learn: topics covered and learning objectives3 min
Vendor-neutral courses with complementary demos from SAS3 min
DEMO - Exploring SAS® Visual Data Mining and Machine Learning (optional)11 min
Deep learning: your path towards leveraging the hottest ML method4 min
A tour of this specialization's courses4 min
About your instructor, Eric Siegel7 min
2 lectures
The Machine Learning Glossary (optional)10 min
One-question survey1 min
4 heures pour terminer

MODULE 1 - The Impact of Machine Learning

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

Semaine 2

2 heures pour terminer

MODULE 2 - Data: the New Oil

2 heures pour terminer
11 vidéos (Total 63 min), 1 lecture, 11 quiz
11 vidéos
A paradigm shift for scientific discovery: its automation5 min
Example discoveries from data6 min
The Data Effect: Data is always predictive4 min
Training data -- what it looks like6 min
Predicting with one single variable4 min
Growing a decision tree to combine variables6 min
More on decision trees5 min
The light bulb puzzle4 min
Measuring predictive performance: lift6 min
DEMO - Training a simple decision tree model (optional)9 min
1 lecture
How spending habits reveal debtor reliability (optional)5 min
11 exercices pour s'entraîner
The big deal about big data2 min
A paradigm shift for scientific discovery: its automation2 min
Example discoveries from data2 min
The Data Effect: Data is always predictive2 min
Training data -- what it looks like4 min
Predicting with one single variable2 min
Growing a decision tree to combine variables2 min
More on decision trees2 min
The light bulb puzzle4 min
Measuring predictive performance: lift2 min
Module 2 Review30 min
Semaine
3

Semaine 3

3 heures pour terminer

MODULE 3 - Predictive Models: What Gets Learned from Data

3 heures pour terminer
11 vidéos (Total 70 min), 4 lectures, 11 quiz
11 vidéos
How can you trust a predictive model (train/test)?5 min
More predictive modeling principles 6 min
Visually comparing modeling methods - decision boundaries5 min
DEMO - Training and comparing multiple models (optional)8 min
Deploying a predictive model8 min
The profit curve of a model7 min
Deployment results in targeting marketing and sales6 min
Deep learning - application areas and limitations6 min
Labeled data: a source of great power, yet a major limitation5 min
Talking computers -- natural language processing and text analytics4 min
4 lectures
Prescriptive vs. Predictive Analytics – A Distinction without a Difference (optional)5 min
Predictive analytics deployment and profit (optional)5 min
More on deep learning (optional)15 min
The difference between Watson and Siri (optional) 5 min
11 exercices pour s'entraîner
The principles of predictive modeling3 min
How can you trust a predictive model (train/test)?2 min
More predictive modeling principles 2 min
Visually comparing modeling methods - decision boundaries2 min
Deploying a predictive model2 min
The profit curve of a model2 min
Deployment results in targeting marketing and sales2 min
Deep learning - application areas and limitations2 min
Labeled data: a source of great power, yet a major limitation2 min
Talking computers – natural language processing and text analytics2 min
Module 3 Review30 min
Semaine
4

Semaine 4

3 heures pour terminer

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

3 heures pour terminer
10 vidéos (Total 70 min), 4 lectures, 10 quiz
10 vidéos
Dismantling the logical fallacy that is AI6 min
Why legitimizing AI as a field incurs great cost6 min
Ethics overview: five ways ML threatens social justice9 min
Blatantly discriminatory models7 min
The trend towards discriminatory models6 min
The argument against discriminatory models7 min
Five myths about "evil" big data8 min
Defending machine learning -- how it does good6 min
Course wrap-up3 min
4 lectures
AI is a big fat lie (optional) 10 min
AI is an ideology, not a technology (optional)10 min
Book Review: Weapons of Math Destruction by Cathy O'Neil15 min
Coded gaze on speech recognition (optional)5 min
10 exercices pour s'entraîner
Why machine learning isn't becoming superintelligent2 min
Dismantling the logical fallacy that is AI2 min
Why legitimizing AI as a field incurs great cost2 min
Ethics overview: five ways ML threatens social justice2 min
Blatantly discriminatory models4 min
The trend towards discriminatory models2 min
The argument against discriminatory models8 min
Five myths about "evil" big data5 min
Defending machine learning -- how it does good2 min
Module 4 Review 30 min

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À propos du Spécialisation Machine Learning for Everyone with Eric Siegel

Machine Learning for Everyone with Eric Siegel

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