Artificial Intelligence (AI) Foundation

This courseware is available in a Virtual Classroom configuration.

The CCC’s Artificial Intelligence or AI Foundation course provides an overview and insight into the critical emerging technology of Artificial Intelligence to organizations and individuals…

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Certificate Artificial Intelligence Foundation
Duration 16 Hours
Delivery Online, Self-study
Accreditor Cloud Credential Council
Languages English

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What is included in Artificial Intelligence (AI) Foundation

Course Description

The CCC’s Artificial Intelligence or AI Foundation course provides an overview and insight into the critical emerging technology of Artificial Intelligence to organizations and individuals around the world.

This vendor-neutral, cross-industry foundation course:

  • Builds upon the AI definition, its evolution, concepts, and applications. 
  • Provides an introduction to machine learning concepts.
  • Explains the main business drivers and strategies to leverage AI.
  • Introduces the fundamentals of the essential prerequisite skills required for AI, such as databases, statistics, data visualization, the Python programming language, algorithms, and data structures.
  • Delves into the different strategies for the implementation of data structures.

Course Information

Certification: Artificial Intelligence Foundation

Duration: 16 Hours

Domain: IT Governance and Strategy

Delivery Method: Online, Self-study

Accreditor: Cloud Credential Council

Available Languages: English

Purchase Options: Pay Per Use Courseware


The target audience for this course includes all teams across the Business and IT functions, including:

  • C – Level Executives and Senior Management
  • General Managers including Business Development Managers 
  • IT Project, Program, and Planning Managers
  • AI Project Managers
  • Service Architects and Managers
  • Business Strategists and Analysts, Business Change Practitioners and Managers
  • Data Analysts, Data Engineers, and Data Scientists
  • Process Architects and Managers
  • Consultants, Professionals in varied fields

Learning Objectives

AI Foundation-certified professionals can:

  • Explain the concepts, terminologies, evolution, and business drivers of AI.
  • Explain the fundamentals of Machine Learning.
  • Explain the fundamentals of relational databases and the SQL database language.
  • Explain the fundamentals of statistics and data visualization.
  • Explain the fundamentals of the Python programming language.
  • Explain the concepts of Algorithms and Data Structures.
  • Discuss the different implementation strategies for Data Structures.

Course Outline

Module 0: Course Introduction

  • Course Highlights
  • Course Learning Outcomes
  • Module Structure
  • Exam Overview

Module 1: AI Definition, Evolution, and Concepts

  • Introduction to Artificial Intelligence
    • Overview of AI
    • What is intelligence?
    • What makes a machine intelligent?
    • Definitions of AI
    • Drivers of AI
  • Basics of Artificial Intelligence
    • Evolution AI – Industry 4.0
    • Principles of AI
    • Artificial Intelligence – Continuum
    • Types of AI
    • Major Benefits of AI
    • How the term AI is being misused?
    • Top AI Use Cases
  • Fundamentals of Machine Learning
    • What is Machine Learning?
    • Machine Learning – An Example
    • Companies Using Machine Learning
    • How Machine Learning works?
    • How do you decide which Machine Learning algorithm to use?

Module 2: Fundamentals of Databases

  • Basics of Databases
    • AI and Databases
    • Overview of Data and Databases
    • Hierarchical Database Model
    • Network Database Model
    • Relational Database Model
    • Object Oriented Database Model
  • Concepts of Relational Databases
    • Overview of Relational Databases
    • Entity Relational Diagram
    • Normalization
  • Database Languages
    • Overview
    • DDL Commands
    • DML Commands
    • TCL Commands
    • DCL Commands
    • Structured Query Language (SQL)
  • Data Types and Constraints
    • Data Types
    • Constraints
  • SQL Commands
    • CREATE, SHOW, and DROP DATABASE Commands
    • Applying Constraints of a Table
    • SQL Comments
  • SQL Keywords
  • SQL Operators
    • AND, OR, and NOT
    • IN, BETWEEN, and LIKE
  • SQL Functions and Additional Objects
    • Min and Max Functions
    • Count and Average Functions
    • SQL Aliases
    • Transactions
  • SQL Joins
    • Overview
    • Join Examples

Module 3: Fundamentals of Statistics

  • Basics of Statistics
    • Statistics and AI
    • Definition and Types of Statistics 
    • Descriptive Statistics
    • Measures of Central Tendency
    • Median
    • Mode
    • Measures of Central Dispersion
  • Fundamentals of Data Visualization
    • Data Visualization
    • Data Visualization – An Example
    • Principles of Data Visualization
    • Types of Data Visualization
  • Visual Data Visualization Types
    • Dashboards
    • Best Practices for Developing Intuitive Dashboards
    • Graph Types
    • Pie Charts
    • Line Graphs
    • Scatter Plots
    • Bar Charts
    • Column Charts
    • Ring or Donut Plots
  • Data Visualization Popular Tools
    • Tableau
    • QlikView
    • D3.js
    • ggplot
    • Bokeh
    • Plotly
    • Pygal
    • Altair
    • Geoplotlib

Module 4: Python Programming Fundamentals

  • Introduction and Evolution of Python
    • AI and Programming
    • Brief Introduction to Python
    • Evolution of Python
    • Applications of Python
  • Python Programming Concepts
    • Python Programming Environment
    • Indentation
    • Comments
  • Python Object Types
    • Variables
    • Data Types
    • Numbers
    • Operators
    • Arithmetic Operators
    • Relational Operators
    • Collection (Arrays)
    • Lists
    • Tuples
    • Sets
    • Dictionaries
  • Python Programming Concepts
    • Conditional Statements
    • Loops
  • Python Debugging Concepts
    • Overview of Debugging with an Example

Module 5: Foundation and Implementation of Data Structures and Algorithms

  • Fundamentals of Data Structures
    • What is a Data Structure?
    • Primitive Data Structures (Integer, Floating Point, Character, Boolean)
    • Non Primitive Data Structures (Array, Stack, Linked List, Queue, Tree, Graph, Set, Map, String)
  • Fundamentals of Algorithms
    • Standard Template Library (STL)
    • Components of STL (Algorithms, Containers, STL Functions, STL Iterators)
    • Getting Acquainted with Algorithms 
      • Qualities and Types
      • Sorting and Searching Algorithms
      • Numerical Algorithms
      • String Algorithms
      • Geometric Algorithms
      • Graph Algorithms

Available Courseware For Artificial Intelligence (AI) Foundation


Exam & Exam Preparation

Official Cloud Credential Council exam and additional mock exam that can be taken by the learner so as to help prepare the learner to pass the exam.


Course Videos

Videos that can be used in a classroom, blended, and self-study environment that explain the key elements of the course.

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