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.

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

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.

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
  • CREATE, DESCRIBE, ALTER, DROP, TRUNCATE, RENAME TABLE Commands
  • Applying Constraints of a Table
  • SQL Comments
  • INSERT INTO, UPDATE, DELETE, and SELECT Commands
• SQL Keywords
  • SQL WHERE CLAUSE
  • SQL ORDER BY
  • SQL GROUP BY
  • SQL HAVING
• 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
  • COMMIT and ROLLBACK
• 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