AI Essentials Training for Beginners is a practical, beginner-friendly program designed to give you a strong foundation in Artificial Intelligence and its real-world applications. Whether you are exploring AI for the first time, preparing for a tech career, or looking to enhance your professional skills, this course offers a clear and structured introduction without overwhelming technical complexity. 

Through guided lessons, hands-on exercises, and real examples, you’ll learn how AI systems work, what machine learning models do, how data powers intelligent solutions, and how modern AI tools—such as ChatGPT, Azure AI, and other industry platforms—are used across businesses. You’ll also explore ethical considerations, AI-driven decision making, and how organizations integrate AI to improve productivity and innovation. 

By the end of this course, you’ll not only understand the key concepts behind AI but also be equipped with practical skills to begin using AI tools confidently in everyday tasks, projects, or workplace settings. This course sets a strong foundation for students who want to continue into more advanced tracks like Machine Learning with Python, Agentic AI, or Applied AI for Business. 

This course is designed for absolute beginners, so no prior experience in programming, math, or AI is required. However, the following basic skills will help you get the most out of the training: Comfort using a computer (navigating files, using browser tools, basic typing) Familiarity with Microsoft Office tools (Excel or Word—basic level) 

Interest in technology, problem-solving, or data-driven decision making Willingness to learn new tools and follow hands-on exercises This makes the course accessible to students, professionals, job seekers, non-technical learners, and anyone exploring AI for the first time. 

By the end of this course, you will be able to: 

• Understand what Artificial Intelligence is and how it is used across industries 

• Explain the difference between AI, Machine Learning, Deep Learning, and Data Science 

• Identify key branches of AI including NLP, Computer Vision, and Robotics 

• Recognize real-world AI applications in healthcare, retail, finance, and education 

• Understand the basics of Machine Learning including supervised, unsupervised, and reinforcement learning 

• Explain core ML concepts such as features, labels, training, testing, and overfitting 

• Describe common algorithms like linear regression, decision trees, and clustering 

• Understand how NLP works and apply tasks such as sentiment analysis and text classification 

• Compare popular Generative AI tools such as ChatGPT, Gemini, Claude, Copilot, and Perplexity 

• Use AI tools for productivity—summarization, content creation, automation, and communication 

• Explore major AI platforms such as Azure AI, Vertex AI, SageMaker, and Watson 

• Work with low-code/no-code tools to build simple AI or ML models 

• Apply effective prompt engineering techniques to get better results from AI tools 

• Understand how AI impacts business workflows, teams, and job roles 

• Recognize future AI trends, ethical considerations, and emerging career opportunities 

Learning AI fundamentals positions you for various beginner-level roles in technology, business, and analytics environments. After completing this course, learners will be better prepared for positions such as: 

• AI Assistant / AI Support Specialist (Entry-Level) 
• Junior Data Analyst 
• Business Analyst (Entry-Level) 
• AI Productivity Specialist 
• Prompt Engineer (Beginner-Level) 
• Automation Assistant / Workflow Automation Analyst 
• Customer Support Analyst (AI-enabled support tools) 
• Digital Operations Assistant 
• IT Support Analyst (with AI tool responsibilities) 
• Research Assistant (AI or ML projects in academic or corporate teams) 

Module 1: Introduction to Artificial Intelligence

• What is Artificial Intelligence?

• History and evolution of AI

• Branches of AI:

  1. Machine Learning

  2. Deep Learning

  3. Natural Language Processing (NLP)

  4. Computer Vision

  5. Robotics

• AI vs. Machine Learning vs. Data Science

• Real-life applications in:

  1. Healthcare

  2. Retail

  3. Finance

  4. Education

• Ethical considerations and responsible AI

Module 2: Machine Learning Basics

• Understanding Machine Learning

• Types of Machine Learning:

  1. Supervised Learning

  2. Unsupervised Learning

  3. Reinforcement Learning

• Key ML concepts:

  1. Features

  2. Labels

  3. Training vs. Testing

  4. Overfitting

• Overview of common ML algorithms:

  1. Linear Regression

  2. Decision Trees

  3. Clustering

• Practical business use cases

• Demo: Simple Machine Learning workflow

Module 3: Natural Language Processing (NLP) & Generative AI

• What is NLP and how it works

• NLP tasks:

  1. Text classification

  2. Sentiment analysis

  3. Chatbot fundamentals

• Introduction to Generative AI (ChatGPT, Bard/Gemini, Claude, Copilot, Perplexity)

• Comparison of top Generative AI tools:

  1. ChatGPT (OpenAI)

  2. Gemini (Google)

  3. Claude (Anthropic)

  4. Microsoft Copilot

  5. Perplexity AI

• Use cases:

  1. Customer support

  2. Content creation

  3. Summarization and automation

Module 4: AI Tools and Platforms

• Overview of major AI platforms:

  1. Microsoft Azure AI

  2. Google Vertex AI

  3. Amazon SageMaker

  4. IBM Watson

• Introduction to open-source frameworks:

  1. TensorFlow

  2. PyTorch

  3. Scikit-learn

• Low-code / no-code AI tools:

  1. Teachable Machine

  2. Lobe.ai

  3. Runway ML

• AI integrated into productivity tools:

  1. Excel/Word Copilot

  2. Gmail Smart Compose

  3. AI image generation tools

Module 5: AI in Practice & Future Trends

• AI in the workplace: automation, efficiency, and productivity

• AI-powered assistants and copilots

• How companies adopt AI: roles, teams, and responsibilities

• Demo: Prompt Engineering — good vs. bad prompts

• Future of AI:

  1. AGI

  2. Regulations

  3. Impact on jobs and new career opportunities