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AI for Executives: The Basics

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AI for Executives: The Basics

This course is part of AI for Executives Specialization

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Gain insight into a topic and learn the fundamentals.
Beginner level

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2 weeks to complete
at 10 hours a week
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Learn at your own pace

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Map executive decisions to the right AI/ML methods; distinguish algorithms vs. models.

  • Build a governance-ready data strategy—data quality, anonymity, and privacy—for AI projects.

  • Plan AI pipelines and evaluate/select models—including when to reuse LLMs and off-the-shelf options.

Details to know

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Assessments

6 assignments

Taught in English

Build your subject-matter expertise

This course is part of the AI for Executives Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 5 modules in this course

AI for Executives: The Basics gives managers a practical, non-technical introduction to artificial intelligence and machine learning for business decision-making. You’ll learn how AI fits into executive strategy, what ML models can and can’t do, and how to lead data-driven initiatives that create measurable value. Starting with the fundamentals, the course explains algorithms vs. models, core ML tasks, and the lifecycle for building and governing solutions. You’ll then design a data strategy—covering data quality, privacy, and responsible use—before applying techniques such as regression, decision trees, and modern large language models (LLMs) to real executive-level use cases. Finally, you’ll put it together by planning AI pipelines, evaluating model performance and non-functional properties, and knowing when to customize or reuse off-the-shelf models. Hands-on assignments use familiar tools and require no coding. By the end, you’ll be able to map business problems to the right AI approach, communicate with technical teams, and build an informed roadmap for adopting AI across your organization.

This module provides a foundational understanding of the crucial role Machine Learning plays in shaping executive decision-making processes. Participants will explore the core concepts of Machine Learning, uncovering its strategic significance in influencing high-level business decisions. The module offers a comprehensive exploration of the fundamental principles that govern the application of Machine Learning in an executive/business context.

What's included

11 videos7 readings2 assignments1 ungraded lab

11 videosTotal 43 minutes
  • Introduction to the Specialization2 minutes
  • Introduction to Course One1 minute
  • General Notions on Decision Making5 minutes
  • Before AI: Business Data Analysis by Statistics4 minutes
  • Data Descriptive Statistics4 minutes
  • Data Bivariate and Multivariate Statistics5 minutes
  • Decision Making via Statistics and Algorithms3 minutes
  • Decision Making via AI3 minutes
  • Introduction to Machine Learning (ML) Tasks5 minutes
  • Model Validation4 minutes
  • The Machine Learning Tasks6 minutes
7 readingsTotal 70 minutes
  • Before AI: Business Data Analysis by Statistics Key Topics10 minutes
  • Data Descriptive Statistics Key Topics10 minutes
  • Data Bivariate and Multivariate Statistics Key Topics10 minutes
  • Decision Making via Statistics and Algorithms Key Topics10 minutes
  • Decision Making via AI Key Topics10 minutes
  • Model Validation Key Topics10 minutes
  • The Machine Learning Tasks Key Topics10 minutes
2 assignmentsTotal 210 minutes
  • Module 1 Quiz30 minutes
  • Google Sheets Output Assignment (Checker)180 minutes
1 ungraded labTotal 60 minutes
  • Lab 1: Performing Basic Statistics Using Absenteeism Dataset60 minutes

What's included

5 videos2 readings1 assignment1 ungraded lab

5 videosTotal 20 minutes
  • Introduction to Data Provisioning and Management5 minutes
  • Data Strategy Objectives and Data Preparation4 minutes
  • How Data Lakes Support Business Ready AI4 minutes
  • Designing the Data Architecture for Machine Learning5 minutes
  • Bivariate Filtering Method and Data Improvement Techniques3 minutes
2 readingsTotal 20 minutes
  • Data Strategy Objectives and Data Preparation Key Topics10 minutes
  • Bivariate Filtering Method and Data Improvement Techniques Key Topics10 minutes
1 assignmentTotal 30 minutes
  • Module 2 Quiz30 minutes
1 ungraded labTotal 60 minutes
  • Lab 2: Improving Data Quality via Interpolation.60 minutes

What's included

14 videos14 readings1 assignment2 ungraded labs

14 videosTotal 50 minutes
  • Linear Regression4 minutes
  • Linear Regression Model Significance3 minutes
  • Improving the Quality of a Linear Regression Model3 minutes
  • Multiple Regression7 minutes
  • Multiple Regression Model Significance3 minutes
  • Interactions Between Independent Variables in Multiple Regression1 minute
  • Decision Trees - Part 15 minutes
  • Decision Trees - Part 22 minutes
  • The K-Nearest Neighbors3 minutes
  • Support Vector Machines (SVM)4 minutes
  • The Fundamentals of Building Language Models2 minutes
  • Training and Deploying Language Models4 minutes
  • Techniques to Improve Language Models5 minutes
  • Improving The Generalization Capabilities of Language Models4 minutes
14 readingsTotal 140 minutes
  • Linear Regression Key Topics10 minutes
  • Linear Regression Model Significance Key Topics10 minutes
  • Improving the Quality of a Linear Regression Model Key Topics10 minutes
  • Multiple Regression Key Topics10 minutes
  • Multiple Regression Model Significance Key Topics10 minutes
  • Interactions Between Independent Variables in Multiple Regression Key Topics10 minutes
  • Decision Trees - Part 1 Key Topics10 minutes
  • Decision Trees - Part 2 Key Topics10 minutes
  • The K-Nearest Neighbors Key Topics10 minutes
  • Support Vector Machines (SVM) Key Topics10 minutes
  • The Fundamentals of Building Language Models Key Topics10 minutes
  • Training and Deploying Language Models Key Topics10 minutes
  • Techniques to Improve Language Models Key Topics10 minutes
  • Improving The Generalization Capabilities of Language Models Key Topics10 minutes
1 assignmentTotal 30 minutes
  • Module 3 Quiz30 minutes
2 ungraded labsTotal 120 minutes
  • Lab 3: Building and Evaluating a Regression Model60 minutes
  • Lab 4: LLM: How Does it Work?60 minutes

What's included

11 videos12 readings1 assignment

11 videosTotal 43 minutes
  • Decision Tree Induction3 minutes
  • Entropy and Information Gain in Decision Tree Induction5 minutes
  • Information Gain for Continuous Value Attributes4 minutes
  • Gini Index and Impurity Reduction3 minutes
  • Introduction to Deep Learning3 minutes
  • Convolutional Neural Networks4 minutes
  • How Convolution Works2 minutes
  • Convolutional vs Fully Connected Architectures3 minutes
  • CNN for Tabular Data5 minutes
  • Introduction to Autoencoders6 minutes
  • Time Series Data5 minutes
12 readingsTotal 120 minutes
  • Decision Tree Induction Key Topics10 minutes
  • Entropy and Information Gain in Decision Tree Induction Key Topics10 minutes
  • Information Gain for Continuous Value Attributes Key Topics10 minutes
  • Gini Index and Impurity Reduction Key Topics10 minutes
  • Introduction to Deep Learning Key Topics10 minutes
  • Convolutional Neural Networks Key Topics10 minutes
  • How Convolution Works Key Topics10 minutes
  • Convolutional vs Fully Connected Architectures Key Topics10 minutes
  • CNN for Tabular Data Key Topics10 minutes
  • Introduction to Autoencoders Key Topics10 minutes
  • Introduction to Time-Series Prediction Models10 minutes
  • Time Series Data Key Topics10 minutes
1 assignmentTotal 30 minutes
  • Module 4 Quiz30 minutes

What's included

4 videos4 readings1 assignment

4 videosTotal 17 minutes
  • Wrap Up Executive Summary5 minutes
  • AI Key Success Factors6 minutes
  • Design of AI-ML Pipelines5 minutes
  • Course One Conclusion1 minute
4 readingsTotal 40 minutes
  • Wrap up Executive Summary Key Topics10 minutes
  • AI Key Success Factors Key Topics10 minutes
  • Publicly Available Models10 minutes
  • Retraining and Maintenance10 minutes
1 assignmentTotal 30 minutes
  • Module 5 Quiz30 minutes

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Instructor

Khalifa University
3 Courses2,450 learners

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