Vendor: Cloud Credential Council
Mode: Classroom or Virtual with Instructor
Level: Intermediate
Exam: Artificial Intelligence Foundation Exam
Exam at your place: yes, ask for a quote
Duration: 2 days
Language: English, Italian, (German and French on request)

Course Price

CHF 1'270.00

(excl. VAT)

Discount available for multiple students and dedicated classes.

Course Schedule
Ask for more dates, other languages or a different delivery mode as needed and we will do our best to meet your needs.
LanguageModeStart Date
GermanVirtual Class or ClassroomOn request
FrenchVirtual Class or ClassroomOn request
ItalianVirtual Class or ClassroomOn request
EnglishVirtual Class or ClassroomOn request

What you will get

  • Attend one day training with instructor in presence classroom or virtual class
  • Student guide
  • Mock lab exam
  • Exam voucher

Course Overview

The CCC’s Artificial Intelligence (AI) Foundation™ course offers a comprehensive introduction to the pivotal technology of Artificial Intelligence for both organizations and individuals globally.

This vendor-neutral course, applicable across various industries, covers the following key areas:

  1. Expands on the definition and evolution of AI, along with its fundamental concepts and practical applications.
  2. Provides a beginner-level understanding of machine learning concepts.
  3. Discusses the primary business motivations and approaches for maximizing AI benefits.
  4. Introduces the fundamental skills necessary for AI, including databases, statistics, data visualization, Python programming, algorithms, and data structures.
  5. Explores diverse strategies for implementing and utilizing data structures effectively.

Certification Exam

CCC certified professionals play a pivotal role in facilitating organizational change and innovation. Their verifiable skills and credentials set them apart from their peers, enabling them to make a tangible impact within their organizations. Embark on your transformative journey with the career-enhancing Artificial Intelligence Foundation™ course today.

  • Delivery: Online
  • Format: Closed Book
  • Proctoring: Web proctored
  • Duration: 60 minutes (15 minutes additional for non-native English Speakers)
  • No. of Questions: 40 simple multiple choice
  • Pass Grade: 65%


  • A foundational understanding of high-level programming languages such as Python and R is required.
  • (Reccomended) basic familiarity with concepts including cloud services, relational databases, algebra and statistics, algorithms, data structures, data visualization, and proficiency in a high-level programming language.

Course Audience

The course is designed for a wide range of professionals across both 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 and Professionals in various fields

What Skills Will You Learn?

  1. Concepts, Terminologies, Evolution, and Business Drivers of AI:
    • Overview of AI: Definition, scope, and applications.
    • Evolution of AI: Historical background and advancements.
    • Terminologies: Understanding key terms such as machine learning, neural networks, natural language processing, etc.
    • Business Drivers: Identifying reasons organizations adopt AI, including efficiency improvement, decision-making support, and competitive advantage.
  2. Fundamentals of Machine Learning:
    • Introduction to Machine Learning: Understanding the concept of learning from data without being explicitly programmed.
    • Types of Machine Learning: Supervised learning, unsupervised learning, and reinforcement learning.
    • Algorithms and Models: Overview of popular algorithms such as linear regression, decision trees, support vector machines, etc.
  3. Fundamentals of Relational Databases and SQL:
    • Relational Databases: Understanding the structure and principles of relational databases.
    • SQL Language: Introduction to SQL for querying and managing relational databases, covering basic commands like SELECT, INSERT, UPDATE, DELETE, etc.
  4. Fundamentals of Statistics and Data Visualization:
    • Statistics Basics: Overview of key statistical concepts such as mean, median, mode, variance, standard deviation, probability distributions, hypothesis testing, etc.
    • Data Visualization: Importance and techniques for presenting data visually using charts, graphs, and other visualization tools.
  5. Fundamentals of the Python Programming Language:
    • Python Basics: Introduction to Python syntax, data types, control structures (if statements, loops), functions, and modules.
    • Libraries for Data Science: Overview of popular Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn for data manipulation, analysis, and visualization.
  6. Concepts of Algorithms and Data Structures:
    • Algorithms: Understanding algorithms as step-by-step procedures for solving problems, including searching, sorting, and optimization algorithms.
    • Data Structures: Overview of fundamental data structures such as arrays, linked lists, stacks, queues, trees, and graphs.
  7. Different Implementation Strategies for Data Structures:
    • Implementation Choices: Understanding the trade-offs between different data structure implementations in terms of time complexity, space complexity, and ease of use.
    • Examples and Applications: Exploring real-world scenarios where different data structures are employed and understanding the selection criteria for each.

Course Layout

Module 0: Course Introduction

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

Module 1: AI Definition, Evolution, and Concepts

  • Introduction to Artificial Intelligence
  • Basics of Artificial Intelligence
  • Fundamentals of Machine Learning

Module 2: Fundamentals of Databases

  • Basics of Databases
  • Concepts of Relational Databases
  • Database Languages
  • Data Types and Constraints
  • SQL Commands
  • SQL Keywords
  • SQL Operators
  • SQL Functions and Additional Objects
  • SQL Joins

Module 3: Fundamentals of Statistics

  • Basics of Statistics
  • Fundamentals of Data Visualization
  • Visual Data Visualization Types
  • Data Visualization Popular Tools

Module 4: Python Programming Fundamentals

  • Introduction and Evolution of Python
  • Python Programming Concepts
  • Python Object Types
  • Python Programming Concepts
  • Python Debugging Concepts

Module 5: Foundation and Implementation of Data Structures and Algorithms

  • Fundamentals of Data Structures
  • Fundamentals of Algorithms

Module 6: Mock Exam

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