AI-100 Artificial Intelligence Fundamentals is a practical training program designed to provide participants with a strong foundation in artificial intelligence, machine learning, generative AI, and real-world AI applications. The course is ideal for professionals, technology teams, and learners who want to understand how AI technologies work and how they can be applied to solve business and organizational challenges.

 

AI-100: Artificial Intelligence Fundamentals and Applications Course Overview

The AI-100 Training Course provides a comprehensive introduction to artificial intelligence and its practical applications. It explores essential AI concepts, machine learning, deep learning, natural language processing, computer vision, generative AI, and responsible AI.

Participants will learn how AI systems work, examine common tools and techniques, identify suitable business use cases, and develop basic AI solutions through guided activities and practical exercises. The course also addresses the ethical, privacy, security, and governance considerations involved in adopting AI.

 

Duration 5 Days – 35 hrs.

 

Learning Objectives

• Explain the fundamental concepts, terminology, and capabilities of artificial intelligence.
• Distinguish among artificial intelligence, machine learning, deep learning, and generative AI.
• Describe the typical stages of an AI solution lifecycle.
• Identify appropriate AI use cases for different industries and business functions.
• Understand the importance of data preparation, quality, and governance in AI projects.
• Explain supervised, unsupervised, and reinforcement learning.
• Describe the basic principles of neural networks and deep learning.
• Recognize common applications of natural language processing and computer vision.
• Apply effective prompt-engineering techniques when using generative AI tools.
• Develop and evaluate basic machine-learning and AI solutions.
• Interpret commonly used AI model evaluation metrics.
• Identify ethical, privacy, security, bias, and transparency concerns in AI systems.
• Develop a basic plan for the responsible implementation of an AI solution.

 

Target Audience 

• Business professionals seeking a practical understanding of AI
• Managers and team leaders responsible for digital transformation initiatives
• IT professionals and technical support personnel
• Software developers and systems analysts
• Data analysts and aspiring data professionals
• Project managers and product owners involved in AI-related projects
• Business analysts and process-improvement specialists
• Entrepreneurs and decision-makers exploring AI opportunities
• Professionals planning to transition into AI or machine-learning roles
• Students and recent graduates interested in artificial intelligence

 

Prerequisites 

• Basic computer literacy
• Familiarity with common business and information technology concepts
• Basic knowledge of data, spreadsheets, or databases
• Basic programming knowledge, preferably Python, is helpful but not required
• No previous artificial intelligence or machine-learning experience is required

 

Course Outline 

Day 1: Artificial Intelligence Foundations

Module 1: Introduction to Artificial Intelligence

• Definition, history, and evolution of AI
• AI terminology and core concepts
• Narrow AI, general AI, and generative AI
• AI capabilities and limitations
• Current AI trends and applications
• Examples of AI across major industries

 Module 2: AI Technologies and Solution Lifecycle

• Artificial intelligence, machine learning, and deep learning
• Natural language processing and computer vision
• Expert systems, robotics, and intelligent automation
• Components of an AI solution
• Stages of the AI solution lifecycle
• Identifying and prioritizing AI use cases
• Benefits, risks, and implementation challenges

  

Day 2: Data and Machine-Learning Fundamentals

Module 3: Data Foundations for AI

• Role of data in artificial intelligence
• Structured, semi-structured, and unstructured data
• Data collection, labeling, and preparation
• Data cleaning and transformation
• Feature selection and feature engineering
• Training, validation, and test datasets
• Data quality, privacy, and governance

 Module 4: Introduction to Machine Learning

• Machine-learning concepts and workflows
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Classification, regression, and clustering
• Selecting an appropriate algorithm
• Training and testing a basic machine-learning model

  

Day 3: Deep Learning, Natural Language Processing, and Computer Vision

Module 5: Deep Learning Fundamentals

• Introduction to neural networks
• Neurons, layers, weights, and activation functions
• Training and optimization concepts
• Convolutional neural networks
• Recurrent neural networks and transformers
• Common deep-learning applications
• Benefits and limitations of deep learning

 Module 6: Natural Language Processing

• Fundamentals of human language processing
• Text preparation and tokenization
• Text classification and sentiment analysis
• Information extraction and summarization
• Translation, speech recognition, and text generation
• Chatbots and virtual assistants
• Transformer-based language models

 Module 7: Computer Vision

• Fundamentals of image and video analysis
• Image classification
• Object detection and tracking
• Image segmentation
• Optical character recognition
• Facial recognition and biometric considerations
• Practical computer-vision use cases

  

Day 4: Generative AI and AI Solution Development

Module 8: Generative Artificial Intelligence

• Generative AI concepts and capabilities
• Large language models
• Foundation models and multimodal systems
• Common generative AI applications
• Strengths and limitations of generative models
• Hallucinations, accuracy, and verification
• Retrieval-augmented generation and model customization

 Module 9: Prompt Engineering

• Elements of an effective prompt
• Providing context, instructions, and constraints
• Zero-shot and few-shot prompting
• Role-based and structured prompting
• Prompt refinement and iteration
• Evaluating AI-generated responses
• Safe and responsible use of prompts

 Module 10: Building an AI Solution

• Defining the business problem
• Establishing AI solution requirements
• Preparing and selecting data
• Choosing suitable models and tools
• Developing a basic AI workflow
• Integrating AI into applications and business processes
• Deployment, monitoring, and maintenance considerations

  

Day 5: Model Evaluation, Responsible AI, and Implementation Planning

Module 11: AI Model Evaluation and Improvement

• Model performance and business value
• Accuracy, precision, recall, and F1 score
• Confusion matrices
• Overfitting and underfitting
• Model validation and error analysis
• Model optimization and continuous improvement
• Monitoring data and model drift

 Module 12: Responsible AI, Ethics, and Governance

• Principles of responsible AI
• Fairness and bias
• Transparency and explainability
• Privacy and data protection
• Security risks in AI systems
• Human oversight and accountability
• Intellectual property considerations
• AI policies, controls, and governance frameworks

 Module 13: AI Adoption and Implementation Planning

• Assessing organizational AI readiness
• Aligning AI initiatives with business goals
• Evaluating feasibility, value, and risk
• Identifying stakeholders and responsibilities
• Planning an AI proof of concept
• Scaling AI solutions across the organization
• Developing an AI adoption roadmap
• Emerging trends and the future of artificial intelligence