このコースについて

13,108 最近の表示
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

学習内容

  • Apply ML: Identify opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and more

  • Plan ML: Determine the way machine learning will be operationally integrated and deployed, and the staffing and data requirements to get there

  • Greenlight ML: Forecast the effectiveness of a machine learning project and then internally sell it, gaining buy-in from your colleagues

  • Lead ML: Manage a machine learning project, from the generation of predictive models to their launch

習得するスキル

Data ScienceArtificial Intelligence (AI)Machine LearningMachine learning strategy and leadershipPredictive Analytics
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

3時間で修了

MODULE 1 - Business Applications of Machine Learning

3時間で修了
13件のビデオ (合計92分), 4 学習用教材, 14 個のテスト
13件のビデオ
The ingredients of a machine learning application4 分
Risky business: predictive analytics enacts risk management10 分
Response modeling to target marketing6 分
Gains curves for response modeling 7 分
Churn modeling to target customer retention6 分
Case study: targeting ads9 分
Case study: product recommendations8 分
Credit scoring6 分
Five ways insurance companies use machine learning8 分
Fraud detection6 分
Case study: insurance fraud detection6 分
Machine learning for government and healthcare5 分
4件の学習用教材
One-question survey1 分
Retaining new customers, a killer app similar to churn modeling (optional)10 分
More information about named examples (optional) 5 分
Generating compelling text with deep learning (optional)10 分
14の練習問題
Course overview2 分
The ingredients of a machine learning application2 分
Risky business: predictive analytics enacts risk management2 分
Response modeling to target marketing2 分
Gains curves for response modeling 2 分
Churn modeling to target customer retention2 分
Case study: targeting ads2 分
Case study: product recommendations2 分
Credit scoring2 分
Five ways insurance companies use machine learning2 分
Fraud detection2 分
Case study: insurance fraud detection2 分
Machine learning for government and healthcare2 分
Module 1 Review30 分
2

2

3時間で修了

MODULE 2 - Scoping, Greenlighting, and Managing Machine Learning Initiatives

3時間で修了
12件のビデオ (合計67分), 7 学習用教材, 12 個のテスト
12件のビデオ
The six steps for running a ML project4 分
Running and iterating on the process steps7 分
How long a machine learning project takes2 分
Refining the prediction goal5 分
Where to start -- picking your first ML project5 分
Strategic objectives and key performance indicators9 分
Personnel - staffing your machine learning team6 分
Sourcing the staff for a machine learning project4 分
Greenlighting: Internally selling a machine learning initiative5 分
More tips for getting the green light6 分
The most important video about ML ever, period2 分
7件の学習用教材
ML project management pitfalls and best practices (optional)10 分
Choosing the right analytics problem (optional)10 分
Six ways to lower costs with predictive analytics (optional)10 分
Counterpoint: AI success comes through growth, not labor savings (optional)10 分
Top 10 roles in AI and data science (optional)10 分
The analytics engineer (optional)10 分
Need a data scientist? Try building a "DataScienceStein" (optional)10 分
12の練習問題
Project management overview2 分
The six steps for running a ML project2 分
Running and iterating on the process steps4 分
How long a machine learning project takes2 分
Refining the prediction goal2 分
Where to start -- picking your first ML project2 分
Strategic objectives and key performance indicators4 分
Personnel - staffing your machine learning team2 分
Sourcing the staff for a machine learning project2 分
Greenlighting: Internally selling a machine learning initiative2 分
More tips for getting the green light2 分
Module 2 Review30 分
3

3

3時間で修了

MODULE 3 - Data Prep: Preparing the Training Data

3時間で修了
14件のビデオ (合計110分), 2 学習用教材, 15 個のテスト
14件のビデオ
Defining the dependent variable8 分
Refining the predictive goal statement in detail7 分
Identifying the sub-problem8 分
How much data do you need, and how balanced?9 分
A flash from the past: independent variables6 分
Behavioral versus demographic data9 分
Derived variables8 分
Five colorful examples of behavioral data for workforce analytics 6 分
The predictive value of social media data9 分
More social data: population trends and interpreting sentiment4 分
Merging in other sources of data7 分
Data cleansing: what kind of noise is okay?8 分
Data disaster: "High school dropouts are better hires"5 分
2件の学習用教材
It is a mistake to ask the wrong question (optional)10 分
It is a mistake to accept leaks from the future (optional)10 分
15の練習問題
Data prep for-the-win -- why it's absolutely crucial2 分
Defining the dependent variable2 分
Refining the predictive goal statement in detail2 分
Identifying the sub-problem2 分
How much data do you need, and how balanced?4 分
A flash from the past: independent variables4 分
Behavioral versus demographic data2 分
Derived variables2 分
Five colorful examples of behavioral data for workforce analytics 2 分
The predictive value of social media data2 分
More social data: population trends and interpreting sentiment2 分
Merging in other sources of data2 分
Data cleansing: what kind of noise is okay?4 分
Data disaster: "High school dropouts are better hires"2 分
Module 3 Review30 分
4

4

3時間で修了

MODULE 4 - The High Cost of False Promises, False Positives, and Misapplied Models

3時間で修了
9件のビデオ (合計66分), 4 学習用教材, 10 個のテスト
9件のビデオ
More accuracy fallacies: predicting psychosis, criminality, & bestsellers9 分
The cost of false positives and false negatives6 分
Assigning costs: so important, yet so difficult4 分
Machine learning for social good7 分
Predicting pregnancy -- and other sensitive machine inductions9 分
Predatory micro-targeting6 分
Predictive policing in law enforcement and national security10 分
Course wrap-up2 分
4件の学習用教材
More reading related to the accuracy fallacy (optional)1 分
Machine learning for social good - more examples (optional)10 分
Further insights on predicting sensitive attributes (optional)10 分
Further analyses of predictive policing and ML’s effect on the balance of power (optional)10 分
10の練習問題
Accuracy fallacy: orchestrating the media's bogus coverage of ML2 分
More accuracy fallacies: predicting psychosis, criminality, & bestsellers2 分
The cost of false positives and false negatives2 分
Assigning costs: so important, yet so difficult2 分
Machine learning for social good2 分
Predicting pregnancy -- and other sensitive machine inductions2 分
Predatory micro-targeting2 分
Predictive policing in law enforcement and national security2 分
Course wrap-up2 分
Module 4 Review30 分

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