Informações sobre o curso

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Nível iniciante

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.

Aprox. 13 horas para completar
Inglês

Habilidades que você terá

Data ScienceArtificial Intelligence (AI)Machine LearningPredictive AnalyticsEthics Of Artificial Intelligence
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível iniciante

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.

Aprox. 13 horas para completar
Inglês

oferecido por

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SAS

Programa - O que você aprenderá com este curso

Semana
1

Semana 1

1 hora para concluir

MODULE 0 - Introduction

1 hora para concluir
9 vídeos (Total 55 mín.), 1 leitura
9 videos
Specialization overview: Machine Learning for Everyone4min
Why this course isn't "hands-on" & why it's still good for techies anyway8min
What you'll learn: topics covered and learning objectives3min
Vendor-neutral courses with complementary demos from SAS3min
DEMO - Exploring SAS® Visual Data Mining and Machine Learning (optional)11min
Deep learning: your path towards leveraging the hottest ML method4min
A tour of this specialization's courses4min
Your instructor: a rap star stuck in a nerd's body8min
1 leituras
One-question survey1min
4 horas para concluir

MODULE 1 - The Impact of Machine Learning

4 horas para concluir
13 vídeos (Total 79 mín.), 6 leituras, 15 testes
13 videos
The Obama example: forecasting vs. predictive analytics4min
The full definitions of machine learning and predictive analytics5min
Buzzword heyday: putting big data and data science in their place5min
The two stages of machine learning: modeling and scoring5min
Targeting marketing with response modeling5min
The Prediction effect: A little prediction goes a long way5min
Targeted customer retention with churn modeling6min
Why targeting ads is like the movie "Groundhog Day"6min
Another application: financial credit risk7min
Myriad opportunities: the great range of application areas7min
"Non-predictive" applications: detection, classification, and diagnosis5min
Why ML is the latest evolutionary step of the Information Age4min
6 leituras
Nate Silver on misunderstanding election forecasts (optional)10min
Predictive analytics overview25min
Detailed profit calculations for targeted marketing (optional)5min
More information about named examples (optional) 5min
Predictive analytics applications (optional)5min
White paper overviewing the organizational value of predictive analytics15min
15 exercícios práticos
Predicting the president: two common misconceptions about forecasting2min
The Obama example: forecasting vs. predictive analytics2min
The full definitions of machine learning and predictive analytics2min
Buzzword heyday: putting big data and data science in their place2min
The two stages of machine learning: modeling and scoring4min
Targeting marketing with response modeling4min
The Prediction effect: A little prediction goes a long way2min
Targeted customer retention with churn modeling4min
Why targeting ads is like the movie "Groundhog Day"2min
Another application: financial credit risk2min
Myriad opportunities: the great range of application areas2min
"Non-predictive" applications: detection, classification, and diagnosis2min
Why ML is the latest evolutionary step of the Information Age2min
A question about the reading – the organizational value of predictive analytics2min
Module 1 Review 30min
Semana
2

Semana 2

2 horas para concluir

MODULE 2 - Data: the New Oil

2 horas para concluir
11 vídeos (Total 63 mín.), 1 leitura, 11 testes
11 videos
A paradigm shift for scientific discovery: its automation5min
Example discoveries from data6min
The Data Effect: Data is always predictive4min
Training data -- what it looks like6min
Predicting with one single variable4min
Growing a decision tree to combine variables6min
More on decision trees5min
The light bulb puzzle4min
Measuring predictive performance: lift6min
DEMO - Training a simple decision tree model (optional)9min
1 leituras
How spending habits reveal debtor reliability (optional)5min
11 exercícios práticos
The big deal about big data2min
A paradigm shift for scientific discovery: its automation2min
Example discoveries from data2min
The Data Effect: Data is always predictive2min
Training data -- what it looks like4min
Predicting with one single variable2min
Growing a decision tree to combine variables2min
More on decision trees2min
The light bulb puzzle4min
Measuring predictive performance: lift2min
Module 2 Review30min
Semana
3

Semana 3

3 horas para concluir

MODULE 3 - Predictive Models: What Gets Learned from Data

3 horas para concluir
11 vídeos (Total 70 mín.), 4 leituras, 11 testes
11 videos
How can you trust a predictive model (train/test)?5min
More predictive modeling principles 6min
Visually comparing modeling methods - decision boundaries5min
DEMO - Training and comparing multiple models (optional)8min
Deploying a predictive model8min
The profit curve of a model7min
Deployment results in targeting marketing and sales6min
Deep learning - application areas and limitations6min
Labeled data: a source of great power, yet a major limitation5min
Talking computers -- natural language processing and text analytics4min
4 leituras
Prescriptive vs. Predictive Analytics – A Distinction without a Difference (optional)5min
Predictive analytics deployment and profit (optional)5min
More on deep learning (optional)15min
The difference between Watson and Siri (optional) 5min
11 exercícios práticos
The principles of predictive modeling3min
How can you trust a predictive model (train/test)?2min
More predictive modeling principles 2min
Visually comparing modeling methods - decision boundaries2min
Deploying a predictive model2min
The profit curve of a model2min
Deployment results in targeting marketing and sales2min
Deep learning - application areas and limitations2min
Labeled data: a source of great power, yet a major limitation2min
Talking computers – natural language processing and text analytics2min
Module 3 Review30min
Semana
4

Semana 4

3 horas para concluir

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

3 horas para concluir
10 vídeos (Total 70 mín.), 4 leituras, 10 testes
10 videos
Dismantling the logical fallacy that is AI6min
Why legitimizing AI as a field incurs great cost6min
Ethics overview: five ways ML threatens social justice9min
Blatantly discriminatory models7min
The trend towards discriminatory models6min
The argument against discriminatory models7min
Five myths about "evil" big data8min
Defending machine learning -- how it does good6min
Course wrap-up3min
4 leituras
AI is a big fat lie (optional) 10min
AI is an ideology, not a technology (optional)10min
Book Review: Weapons of Math Destruction by Cathy O'Neil15min
Coded gaze on speech recognition (optional)5min
10 exercícios práticos
Why machine learning isn't becoming superintelligent2min
Dismantling the logical fallacy that is AI2min
Why legitimizing AI as a field incurs great cost2min
Ethics overview: five ways ML threatens social justice2min
Blatantly discriminatory models4min
The trend towards discriminatory models2min
The argument against discriminatory models8min
Five myths about "evil" big data5min
Defending machine learning -- how it does good2min
Module 4 Review 30min

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