このコースについて

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スケジュールに従って期限をリセットします。
初級レベル

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.

約13時間で修了
英語

習得するスキル

Data ScienceArtificial Intelligence (AI)Machine LearningPredictive AnalyticsEthics Of Artificial Intelligence
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
初級レベル

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.

約13時間で修了
英語

提供:

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SAS

シラバス - 本コースの学習内容

1

1

1時間で修了

MODULE 0 - Introduction

1時間で修了
9件のビデオ (合計55分), 1 学習用教材
9件のビデオ
Specialization overview: Machine Learning for Everyone4 分
Why this course isn't "hands-on" & why it's still good for techies anyway8 分
What you'll learn: topics covered and learning objectives3 分
Vendor-neutral courses with complementary demos from SAS3 分
DEMO - Exploring SAS® Visual Data Mining and Machine Learning (optional)11 分
Deep learning: your path towards leveraging the hottest ML method4 分
A tour of this specialization's courses4 分
Your instructor: a rap star stuck in a nerd's body8 分
1件の学習用教材
One-question survey1 分
4時間で修了

MODULE 1 - The Impact of Machine Learning

4時間で修了
13件のビデオ (合計79分), 6 学習用教材, 15 個のテスト
13件のビデオ
The Obama example: forecasting vs. predictive analytics4 分
The full definitions of machine learning and predictive analytics5 分
Buzzword heyday: putting big data and data science in their place5 分
The two stages of machine learning: modeling and scoring5 分
Targeting marketing with response modeling5 分
The Prediction effect: A little prediction goes a long way5 分
Targeted customer retention with churn modeling6 分
Why targeting ads is like the movie "Groundhog Day"6 分
Another application: financial credit risk7 分
Myriad opportunities: the great range of application areas7 分
"Non-predictive" applications: detection, classification, and diagnosis5 分
Why ML is the latest evolutionary step of the Information Age4 分
6件の学習用教材
Nate Silver on misunderstanding election forecasts (optional)10 分
Predictive analytics overview25 分
Detailed profit calculations for targeted marketing (optional)5 分
More information about named examples (optional) 5 分
Predictive analytics applications (optional)5 分
White paper overviewing the organizational value of predictive analytics15 分
15の練習問題
Predicting the president: two common misconceptions about forecasting2 分
The Obama example: forecasting vs. predictive analytics2 分
The full definitions of machine learning and predictive analytics2 分
Buzzword heyday: putting big data and data science in their place2 分
The two stages of machine learning: modeling and scoring4 分
Targeting marketing with response modeling4 分
The Prediction effect: A little prediction goes a long way2 分
Targeted customer retention with churn modeling4 分
Why targeting ads is like the movie "Groundhog Day"2 分
Another application: financial credit risk2 分
Myriad opportunities: the great range of application areas2 分
"Non-predictive" applications: detection, classification, and diagnosis2 分
Why ML is the latest evolutionary step of the Information Age2 分
A question about the reading – the organizational value of predictive analytics2 分
Module 1 Review 30 分
2

2

2時間で修了

MODULE 2 - Data: the New Oil

2時間で修了
11件のビデオ (合計63分), 1 学習用教材, 11 個のテスト
11件のビデオ
A paradigm shift for scientific discovery: its automation5 分
Example discoveries from data6 分
The Data Effect: Data is always predictive4 分
Training data -- what it looks like6 分
Predicting with one single variable4 分
Growing a decision tree to combine variables6 分
More on decision trees5 分
The light bulb puzzle4 分
Measuring predictive performance: lift6 分
DEMO - Training a simple decision tree model (optional)9 分
1件の学習用教材
How spending habits reveal debtor reliability (optional)5 分
11の練習問題
The big deal about big data2 分
A paradigm shift for scientific discovery: its automation2 分
Example discoveries from data2 分
The Data Effect: Data is always predictive2 分
Training data -- what it looks like4 分
Predicting with one single variable2 分
Growing a decision tree to combine variables2 分
More on decision trees2 分
The light bulb puzzle4 分
Measuring predictive performance: lift2 分
Module 2 Review30 分
3

3

3時間で修了

MODULE 3 - Predictive Models: What Gets Learned from Data

3時間で修了
11件のビデオ (合計70分), 4 学習用教材, 11 個のテスト
11件のビデオ
How can you trust a predictive model (train/test)?5 分
More predictive modeling principles 6 分
Visually comparing modeling methods - decision boundaries5 分
DEMO - Training and comparing multiple models (optional)8 分
Deploying a predictive model8 分
The profit curve of a model7 分
Deployment results in targeting marketing and sales6 分
Deep learning - application areas and limitations6 分
Labeled data: a source of great power, yet a major limitation5 分
Talking computers -- natural language processing and text analytics4 分
4件の学習用教材
Prescriptive vs. Predictive Analytics – A Distinction without a Difference (optional)5 分
Predictive analytics deployment and profit (optional)5 分
More on deep learning (optional)15 分
The difference between Watson and Siri (optional) 5 分
11の練習問題
The principles of predictive modeling3 分
How can you trust a predictive model (train/test)?2 分
More predictive modeling principles 2 分
Visually comparing modeling methods - decision boundaries2 分
Deploying a predictive model2 分
The profit curve of a model2 分
Deployment results in targeting marketing and sales2 分
Deep learning - application areas and limitations2 分
Labeled data: a source of great power, yet a major limitation2 分
Talking computers – natural language processing and text analytics2 分
Module 3 Review30 分
4

4

3時間で修了

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

3時間で修了
10件のビデオ (合計70分), 4 学習用教材, 10 個のテスト
10件のビデオ
Dismantling the logical fallacy that is AI6 分
Why legitimizing AI as a field incurs great cost6 分
Ethics overview: five ways ML threatens social justice9 分
Blatantly discriminatory models7 分
The trend towards discriminatory models6 分
The argument against discriminatory models7 分
Five myths about "evil" big data8 分
Defending machine learning -- how it does good6 分
Course wrap-up3 分
4件の学習用教材
AI is a big fat lie (optional) 10 分
AI is an ideology, not a technology (optional)10 分
Book Review: Weapons of Math Destruction by Cathy O'Neil15 分
Coded gaze on speech recognition (optional)5 分
10の練習問題
Why machine learning isn't becoming superintelligent2 分
Dismantling the logical fallacy that is AI2 分
Why legitimizing AI as a field incurs great cost2 分
Ethics overview: five ways ML threatens social justice2 分
Blatantly discriminatory models4 分
The trend towards discriminatory models2 分
The argument against discriminatory models8 分
Five myths about "evil" big data5 分
Defending machine learning -- how it does good2 分
Module 4 Review 30 分

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