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
Show info about module content
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
Google Sheets Output Assignment (Checker)•180 minutes
Module 1 Quiz•30 minutes
1 ungraded lab•Total 60 minutes
Lab 1: Performing Basic Statistics Using Absenteeism Dataset•60 minutes
Module 2 - Building A Data Strategy
Module 2•2 hours to complete
Module details
What's included
5 videos2 readings1 assignment1 ungraded lab
Show info about module content
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
Module 3 - AI-Based Decision Making
Module 3•6 hours to complete
Module details
What's included
14 videos14 readings1 assignment2 ungraded labs
Show info about module content
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
Module 4 - AI-Based Prediction and Classifications
Module 4•3 hours to complete
Module details
What's included
11 videos12 readings1 assignment
Show info about module content
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
When will I have access to the lectures and assignments?
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.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
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.