AI for Executives: The Basics
Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
AI for Executives: The Basics
This course is part of AI for Executives Specialization
Instructor: Prof. Ernesto Damiani
1,834 already enrolled
Included with
Ask Coursera
Recommended experience
Recommended experience
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.
Skills you'll gain
- Data Management
- Business Leadership
- Predictive Analytics
- Decision Intelligence
- Data Strategy
- Data Literacy
- Data-Driven Decision-Making
- Predictive Modeling
- Statistical Machine Learning
- Model Evaluation
- AI Enablement
- Model Training
- Data Integration
- Data Governance
- MLOps (Machine Learning Operations)
- Data Quality
- AI Product Strategy
- Artificial Intelligence and Machine Learning (AI/ML)
- Transfer Learning
Tools you'll learn
Details to know
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- 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 videos•Total 43 minutes
- Introduction to the Specialization•2 minutes
- Introduction to Course One•1 minute
- General Notions on Decision Making•5 minutes
- Before AI: Business Data Analysis by Statistics•4 minutes
- Data Descriptive Statistics•4 minutes
- Data Bivariate and Multivariate Statistics•5 minutes
- Decision Making via Statistics and Algorithms•3 minutes
- Decision Making via AI•3 minutes
- Introduction to Machine Learning (ML) Tasks•5 minutes
- Model Validation•4 minutes
- The Machine Learning Tasks•6 minutes
7 readings•Total 70 minutes
- Before AI: Business Data Analysis by Statistics Key Topics•10 minutes
- Data Descriptive Statistics Key Topics•10 minutes
- Data Bivariate and Multivariate Statistics Key Topics•10 minutes
- Decision Making via Statistics and Algorithms Key Topics•10 minutes
- Decision Making via AI Key Topics•10 minutes
- Model Validation Key Topics•10 minutes
- The Machine Learning Tasks Key Topics•10 minutes
2 assignments•Total 210 minutes
- Module 1 Quiz•30 minutes
- Google Sheets Output Assignment (Checker)•180 minutes
1 ungraded lab•Total 60 minutes
- Lab 1: Performing Basic Statistics Using Absenteeism Dataset•60 minutes
What's included
5 videos2 readings1 assignment1 ungraded lab
5 videos•Total 20 minutes
- Introduction to Data Provisioning and Management•5 minutes
- Data Strategy Objectives and Data Preparation•4 minutes
- How Data Lakes Support Business Ready AI•4 minutes
- Designing the Data Architecture for Machine Learning•5 minutes
- Bivariate Filtering Method and Data Improvement Techniques•3 minutes
2 readings•Total 20 minutes
- Data Strategy Objectives and Data Preparation Key Topics•10 minutes
- Bivariate Filtering Method and Data Improvement Techniques Key Topics•10 minutes
1 assignment•Total 30 minutes
- Module 2 Quiz•30 minutes
1 ungraded lab•Total 60 minutes
- Lab 2: Improving Data Quality via Interpolation.•60 minutes
What's included
14 videos14 readings1 assignment2 ungraded labs
14 videos•Total 50 minutes
- Linear Regression•4 minutes
- Linear Regression Model Significance•3 minutes
- Improving the Quality of a Linear Regression Model•3 minutes
- Multiple Regression•7 minutes
- Multiple Regression Model Significance•3 minutes
- Interactions Between Independent Variables in Multiple Regression•1 minute
- Decision Trees - Part 1•5 minutes
- Decision Trees - Part 2•2 minutes
- The K-Nearest Neighbors•3 minutes
- Support Vector Machines (SVM)•4 minutes
- The Fundamentals of Building Language Models•2 minutes
- Training and Deploying Language Models•4 minutes
- Techniques to Improve Language Models•5 minutes
- Improving The Generalization Capabilities of Language Models•4 minutes
14 readings•Total 140 minutes
- Linear Regression Key Topics•10 minutes
- Linear Regression Model Significance Key Topics•10 minutes
- Improving the Quality of a Linear Regression Model Key Topics•10 minutes
- Multiple Regression Key Topics•10 minutes
- Multiple Regression Model Significance Key Topics•10 minutes
- Interactions Between Independent Variables in Multiple Regression Key Topics•10 minutes
- Decision Trees - Part 1 Key Topics•10 minutes
- Decision Trees - Part 2 Key Topics•10 minutes
- The K-Nearest Neighbors Key Topics•10 minutes
- Support Vector Machines (SVM) Key Topics•10 minutes
- The Fundamentals of Building Language Models Key Topics•10 minutes
- Training and Deploying Language Models Key Topics•10 minutes
- Techniques to Improve Language Models Key Topics•10 minutes
- Improving The Generalization Capabilities of Language Models Key Topics•10 minutes
1 assignment•Total 30 minutes
- Module 3 Quiz•30 minutes
2 ungraded labs•Total 120 minutes
- Lab 3: Building and Evaluating a Regression Model•60 minutes
- Lab 4: LLM: How Does it Work?•60 minutes
What's included
11 videos12 readings1 assignment
11 videos•Total 43 minutes
- Decision Tree Induction•3 minutes
- Entropy and Information Gain in Decision Tree Induction•5 minutes
- Information Gain for Continuous Value Attributes•4 minutes
- Gini Index and Impurity Reduction•3 minutes
- Introduction to Deep Learning•3 minutes
- Convolutional Neural Networks•4 minutes
- How Convolution Works•2 minutes
- Convolutional vs Fully Connected Architectures•3 minutes
- CNN for Tabular Data•5 minutes
- Introduction to Autoencoders•6 minutes
- Time Series Data•5 minutes
12 readings•Total 120 minutes
- Decision Tree Induction Key Topics•10 minutes
- Entropy and Information Gain in Decision Tree Induction Key Topics•10 minutes
- Information Gain for Continuous Value Attributes Key Topics•10 minutes
- Gini Index and Impurity Reduction Key Topics•10 minutes
- Introduction to Deep Learning Key Topics•10 minutes
- Convolutional Neural Networks Key Topics•10 minutes
- How Convolution Works Key Topics•10 minutes
- Convolutional vs Fully Connected Architectures Key Topics•10 minutes
- CNN for Tabular Data Key Topics•10 minutes
- Introduction to Autoencoders Key Topics•10 minutes
- Introduction to Time-Series Prediction Models•10 minutes
- Time Series Data Key Topics•10 minutes
1 assignment•Total 30 minutes
- Module 4 Quiz•30 minutes
What's included
4 videos4 readings1 assignment
4 videos•Total 17 minutes
- Wrap Up Executive Summary•5 minutes
- AI Key Success Factors•6 minutes
- Design of AI-ML Pipelines•5 minutes
- Course One Conclusion•1 minute
4 readings•Total 40 minutes
- Wrap up Executive Summary Key Topics•10 minutes
- AI Key Success Factors Key Topics•10 minutes
- Publicly Available Models•10 minutes
- Retraining and Maintenance•10 minutes
1 assignment•Total 30 minutes
- Module 5 Quiz•30 minutes
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Instructor
Offered by
Explore more from Machine Learning
- Status: Free TrialK
Khalifa University
Specialization
- Status: Free TrialU
University of Pennsylvania
Course
- Status: Free TrialU
University of Illinois Urbana-Champaign
Specialization
- Status: Free TrialK
Khalifa University
Course
Why people choose Coursera for their career
Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
More questions
Financial aid available,
