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

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.

This program is suitable for a broad spectrum of professionals, including but not limited to:

·       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: AI Fundamentals and Overview

·       Introduction to AI and notable success case studies

·       Comparative analysis of Human Intelligence and Artificial Intelligence

·       Evolutionary timeline of AI development

·       The role and significance of Intelligent Agents

·       Constraints and limitations of Artificial Intelligence

·       Strategic decision-making using AI

Unit 2: Intelligent Agents and Their Framework

·       Fundamental concepts of AI Agents

·       Classification and characteristics of different agent types

·       Understanding Knowledge Bases and Databases

·       Logical reasoning and inference methodologies

·       Unification principles in AI systems

·       Deductive reasoning and its applications

Unit 3: Machine Learning Methodologies

·       Overview of Supervised and Unsupervised Learning techniques

·       Categorization and segmentation strategies

·       Artificial Neural Networks and their functionality

·       Learning through sample-based approaches

·       Object recognition techniques in AI

·       Feature extraction and classification models

Unit 4: Fuzzy Logic and Its Applications

·       Fundamentals of Fuzzy Logic reasoning

·       Contrasting Fuzziness and Probability

·       Concepts of Fuzzy Sets and governing rules

·       Significance and practical use of Fuzzy Logic in AI

·       Real-world applications of Fuzzy Control Systems

·       Development of a basic machine learning prototype

Unit 5: Genetic Algorithms and Optimization

·       Foundational concepts of Genetic Algorithms

·       The necessity of optimization, maximization, and minimization techniques

·       Mechanisms of Genetic Algorithms and their evolution

·       Key components: Chromosomes, Genes, Selection, Mutation, and Crossover

·       Applications of Genetic Algorithms in problem-solving

·       Practical implementations for optimizing business processes