Course Description
Introduction:
This training program provides an in-depth understanding of Artificial Intelligence (AI) and its applications across various industries. Participants will explore fundamental AI concepts, logical analysis, and machine learning-based solutions, empowering them to make data-driven decisions and optimize business operations.
Course Objectives:
By the end of this course, participants will be able to:
· Cultivate essential AI competencies
· Comprehend planning and logical analysis methodologies
· Articulate how AI replicates human capabilities in categorization and grouping
· Gain insights into designing machine learning-based applications
· Assess and conceptualize AI-driven solutions
· Examine AI ethics, risks, and governance considerations.
· Understand AI’s role in digital transformation and business strategy.
· Learn about AI-driven predictive analytics for informed decision-making.
Target Audience:
This training course is designed for professionals aiming to enhance business strategies and decision-making processes. It is particularly beneficial for individuals in marketing, finance, engineering, and other emerging technological fields.
· Officers responsible for Quality, Safety, Reliability, and Security
· Project Coordinators
· Senior Executives
· Marketing Directors
· Engineers specializing in Instrumentation, Processes, Systems, Electrical, and Mechanical disciplines
· Financial Analysts, Budget Strategists, Policy Advisors, and Decision-Makers
Course Content:
Unit 1: An Overview of Artificial Intelligence
· Introduction to AI and Its Impact on Business
· Comparison: Human Intelligence vs. Artificial Intelligence
· Historical Evolution of AI
· The Role and Function of Intelligent Agents
· Understanding the Boundaries and Limitations of AI
· AI-Driven Intelligent Decision-Making
Unit 2: Intelligent Agents and Their Role in AI
· Understanding AI Agents and Their Functionality
· Types and Classifications of AI Agents
· Differences Between Knowledge-Based and Database Systems
· Logical Reasoning in AI Applications
· The Unification Process in AI
· Deductive Reasoning for Problem Solving
Unit 3: Machine Learning Fundamentals
· Introduction to Supervised and Unsupervised Learning
· Classification and Clustering Techniques in AI
· Fundamentals of Artificial Neural Networks
· Learning from Examples: AI Training Methods
· Object Recognition in AI Systems
· Feature Engineering and Data Classification
Unit 4: Fuzzy Logic and Decision-Making
· Fundamentals of Fuzzy Logic Thinking
· Differentiating Between Fuzziness and Probability
· Fuzzy Sets and Rule-Based Decision Making
· The Importance of Fuzzy Logic in AI Applications
· Real-World Applications of Fuzzy Controllers
· Building a Simple Machine Learning Model Using Fuzzy Logic
Unit 5: Genetic Algorithms and AI Optimization
· Introduction to Genetic Algorithm (GA) Principles
· The Need for AI Optimization in Decision-Making
· How Genetic Algorithms Evolve and Adapt
· Understanding Chromosomes, Genes, Selection, Mutation, and Crossover
· Dimensions for Applying Genetic Algorithms
· Case Studies: Business Optimization Using Genetic Algorithms
Unit 6: Deep Learning and Neural Networks
· Introduction to Deep Learning and Its Applications
· Understanding Convolutional Neural Networks (CNNs)
· Role of Recurrent Neural Networks (RNNs) in AI
· Training Deep Learning Models for Business Use Cases
· The Importance of Data Preprocessing in Deep Learning
· Hands-On: Implementing a Simple Neural Network Model
Unit 7: AI for Business Process Automation
· Introduction to AI-Driven Automation in Business
· Robotic Process Automation (RPA) and AI Integration
· AI in Workflow Management and Optimization
· Case Studies on AI-Based Process Improvement
· Challenges and Considerations in AI Automation
· Best Practices for Implementing AI in Operations
Unit 8: AI Ethics, Risks, and Governance
· Understanding AI Ethics and Responsible AI Practices
· Identifying Risks Associated with AI Implementation
· The Role of AI Governance in Business
· Ensuring Fairness and Transparency in AI Algorithms
· Addressing Bias and Ethical Dilemmas in AI Models
· Compliance with AI Regulations and Industry Standards
Unit 9: AI in Digital Transformation and Strategy
· The Role of AI in Modern Digital Transformation
· AI as a Driver of Business Strategy and Innovation
· Using AI for Competitive Advantage
· AI’s Impact on Customer Experience and Market Analysis
· Leveraging AI for Real-Time Business Insights
· Developing an AI Strategy for Long-Term Growth
Unit 10: Predictive Analytics and AI-Driven Decision Making
· Understanding Predictive Analytics and Its Business Value
· AI’s Role in Data-Driven Decision-Making
· Building Predictive Models for Business Applications
· Case Studies on AI-Powered Business Forecasting
· The Future of AI in Decision Intelligence
· Practical Implementation: Creating an AI-Based Predictive Mod