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