Course Introduction
In today`s digital era, organizations generate vast amounts of data, and artificial intelligence (AI) presents a powerful opportunity to harness this data for maximum value while minimizing costs. By leveraging modern AI technologies, both structured and unstructured data can be analyzed to unlock new insights, enhance decision-making, optimize business performance, and augment human capabilities.

As AI continues to evolve, it introduces new terminology, methodologies, and business processes that are reshaping industries. This course is designed to equip participants with the essential knowledge and skills to become AI leaders within their organizations. It provides a comprehensive understanding of AI concepts, fosters strategic thinking in AI adoption, and enables participants to effectively plan, implement, and evaluate AI-driven projects.

General Course Objective

This course aims to provide participants with a solid foundation in AI, equipping them with the knowledge and skills to integrate AI techniques into the workplace, enhance decision-making, and develop effective AI strategies to drive organizational success.

Course Objectives

• Understand the core concepts of AI and its various applications.

• Implement AI technologies across different business functions.

• Explore key AI algorithms and techniques.

• Apply best practices for managing AI projects and initiatives.

• Assess the skills and competencies needed for successful AI adoption.

Target Audience

This course is ideal for:

• Business executives and leaders driving digital transformation.

• Heads of departments, innovation managers, and technology strategists.

• Data analysts and professionals in information management.

• Business development and strategic planning specialists.

• IT and AI professionals.

• Consultants and experts in digital transformation and AI.

• Entrepreneurs and startup founders exploring AI-driven solutions.

Course Outline

Module 1: Introduction to AI, Machine Learning (ML), and Data Science

• Understanding AI: Concepts and applications.

• AI as an integration of advanced technologies.

• Historical evolution of AI.

• AI fundamentals: Logic, reasoning, and decision-making.

• The role of machine learning in AI development.

Module 2: The Nine Core Building Blocks of AI

• AI algorithms and search engines.

• Supervised learning:

  • Classification techniques (e.g., Naïve Bayes).

  • Regression models (e.g., Linear regression, Decision trees).

• Semi-supervised learning:

  • Algorithms such as Q-Learning and SARSA.

• Unsupervised learning:

  • Clustering techniques (e.g., k-Means, Hierarchical clustering).

Module 3: AI Methodology and Collaborative Team Approaches

• Hands-on exercises with AI building blocks.

• Developing an AI opportunity matrix.

Module 4: Successful AI Applications Across Business Functions

• Core business activities: Operations, marketing, sales, customer service.

• Support functions: Finance, HR, R&D, procurement, management.

Module 5: AI Technologies and Use Cases

• Natural Language Processing (NLP) for text and speech analysis.

• Image recognition and computer vision applications.

• Machine learning models for predictive analytics.

Module 6: Managing AI Projects Successfully

• AI project lifecycle:

  • Identifying business challenges and defining AI solutions.

  • Data exploration and model development.

  • Implementation and performance evaluation.

• Building AI capabilities within an organization.

• Common AI adoption pitfalls and how to avoid them.

Module 7: AI Tools and Roadmap Development

• Overview of AI technologies: R, Python, Spotfire, Hadoop.

• AI platforms: Microsoft Azure, IBM Watson, Google TensorFlow.

• Developing an AI implementation roadmap.