AI & GenAI Fundamentals Training Online Certification Course
Explore the foundations of artificial intelligence and generative AI with AI & GenAI Fundamentals Training by Multisoft Systems. Understand essential AI concepts, machine learning, neural networks, large language models, prompt engineering, and GenAI applications while gaining practical exposure to modern AI tools and real-world business use cases.
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AI & GenAI Fundamentals Training Online Certification Course Course Overview
AI & GenAI Fundamentals Training by Multisoft Systems introduces participants to the essential concepts, technologies, and practical applications shaping today's artificial intelligence landscape. The course establishes a strong foundation in AI, machine learning, deep learning, neural networks, and the relationship between traditional AI and modern Generative AI technologies.
Participants explore how generative AI systems create text, images, code, summaries, and other forms of content. The training explains foundation models, Large Language Models (LLMs), tokens, embeddings, transformers, prompting concepts, and the fundamental processes behind modern GenAI applications in an accessible and structured manner.
The course also emphasizes practical adoption of AI and GenAI across business functions. Learners gain exposure to prompt engineering techniques, AI-assisted productivity, content generation, data analysis, automation scenarios, responsible AI principles, security considerations, and limitations such as hallucinations and bias. Practical exercises and business-oriented scenarios help participants understand how AI can be applied effectively and responsibly in real-world environments.
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AI & GenAI Fundamentals Training Online Certification Course Course curriculum
Curriculum Designed by Experts
AI & GenAI Fundamentals Training by Multisoft Systems introduces participants to the essential concepts, technologies, and practical applications shaping today's artificial intelligence landscape. The course establishes a strong foundation in AI, machine learning, deep learning, neural networks, and the relationship between traditional AI and modern Generative AI technologies.
Participants explore how generative AI systems create text, images, code, summaries, and other forms of content. The training explains foundation models, Large Language Models (LLMs), tokens, embeddings, transformers, prompting concepts, and the fundamental processes behind modern GenAI applications in an accessible and structured manner.
The course also emphasizes practical adoption of AI and GenAI across business functions. Learners gain exposure to prompt engineering techniques, AI-assisted productivity, content generation, data analysis, automation scenarios, responsible AI principles, security considerations, and limitations such as hallucinations and bias. Practical exercises and business-oriented scenarios help participants understand how AI can be applied effectively and responsibly in real-world environments.
- Understand fundamental concepts of artificial intelligence, machine learning, and deep learning.
- Explain the role and capabilities of generative AI.
- Understand the basic working principles of LLMs and foundation models.
- Recognize concepts such as tokens, embeddings, transformers, and model inference.
- Create effective prompts for different business and productivity requirements.
- Apply generative AI tools to content, research, analysis, and workplace tasks.
- Identify practical AI and GenAI opportunities across business functions.
- Evaluate AI-generated responses for relevance, accuracy, and limitations.
- Understand hallucinations, bias, privacy, security, and ethical considerations.
- Apply responsible AI practices while working with generative AI solutions.
Course Prerequisite
- Basic computer and digital literacy
- General understanding of business or technology environments
- Familiarity with common productivity applications
- Interest in artificial intelligence and generative AI technologies
Course Target Audience
- Business professionals
- IT professionals
- Developers and technical professionals beginning their AI journey
- Business analysts
- Project and program managers
- Data and analytics professionals
- Consultants
- Product managers
- Operations professionals
- Marketing and sales professionals
- Digital transformation teams
- Students and graduates interested in AI
- Professionals exploring the use of GenAI in their roles
Course Content
- Understanding Artificial Intelligence
- Evolution and growth of AI
- AI vs. automation
- Narrow AI and general AI concepts
- Core capabilities of AI systems
- Common AI terminology
- AI applications across industries
- Opportunities and limitations of AI
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- Introduction to Machine Learning
- Relationship between AI, ML, and Deep Learning
- Supervised learning
- Unsupervised learning
- Reinforcement learning fundamentals
- Classification and regression concepts
- Neural network fundamentals
- Introduction to Deep Learning
- Training, validation, and testing concepts
- Model performance and evaluation basics
- Overfitting and underfitting
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- Role of data in artificial intelligence
- Structured and unstructured data
- Data collection and preparation
- Data quality fundamentals
- Features and labels
- Training datasets
- Understanding AI models
- Model training and evaluation
- Inference and prediction
- Model deployment fundamentals
- Monitoring AI models
- Overview of the end-to-end AI lifecycle
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- What is generative AI?
- Generative AI vs. traditional AI
- How generative models work
- Understanding foundation models
- Types of generative models
- Text generation
- Image generation
- Code generation
- Audio and multimodal AI concepts
- Common GenAI capabilities
- Enterprise applications of generative AI
- Limitations of generative models
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- Introduction to Large Language Models
- Understanding Natural Language Processing
- Foundation model concepts
- Transformer architecture fundamentals
- Tokens and tokenization
- Embeddings and semantic representations
- Context windows
- Training and pre-training concepts
- Fine-tuning fundamentals
- Model inference
- Understanding model parameters
- Multimodal models
- Hallucinations and model limitations
- Selecting models for different use cases
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- Introduction to prompt engineering
- Anatomy of an effective prompt
- Instructions, context, and expected output
- Zero-shot prompting
- One-shot and few-shot prompting
- Role-based prompting
- Contextual prompting
- Structured prompt techniques
- Controlling response format
- Prompt refinement and iteration
- Prompt templates
- Handling ambiguous responses
- Evaluating prompt effectiveness
- Practical prompting exercises
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- Overview of the GenAI ecosystem
- AI assistants and copilots
- Content creation and summarization
- Information extraction
- Question answering
- Document analysis
- Translation and language applications
- Coding assistance
- Customer service applications
- Marketing and communication use cases
- HR and employee productivity scenarios
- Finance and analytics applications
- Knowledge management
- Workflow productivity with GenAI
- Identifying suitable GenAI use cases
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- Limitations of standalone LLMs
- Introduction to Retrieval-Augmented Generation
- Basic RAG architecture
- Documents and knowledge sources
- Chunking concepts
- Embeddings in RAG
- Vector database overview
- Retrieval and semantic search
- Combining retrieved context with LLMs
- Enterprise knowledge assistants
- Introduction to AI agents
- AI agents vs traditional chatbots
- Tools, actions, and agent workflows
- Single-agent and multi-agent concepts
- Common agentic AI use cases
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- Introduction to Responsible AI
- Fairness and bias
- Transparency and explainability
- Privacy considerations
- Data security
- Intellectual property considerations
- AI hallucinations and misinformation
- Human oversight
- AI risk fundamentals
- Responsible use of generative AI
- AI governance principles
- Enterprise AI policies
- Safe adoption of AI systems
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- Exploring AI-powered applications
- Working with generative AI interfaces
- Creating effective prompts
- Prompt optimization exercises
- Text generation and transformation
- Summarization exercises
- Information extraction scenarios
- Business question-answering use cases
- Exploring a simple RAG workflow
- Understanding an agent-based workflow
- Evaluating GenAI responses
- Identifying hallucinations and inaccuracies
- Applying responsible AI principles
- Designing a basic GenAI business use case
- Final practical scenario
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AI & GenAI Fundamentals Training (MCQ) Assessment
This assessment tests understanding of course content through MCQ and short answers, analytical thinking, problem-solving abilities, and effective communication of ideas. Some Multisoft Assessment Features :
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- Time limits and countdown timers to manage duration.
AI & GenAI Fundamentals Corporate Training
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Why AI & GenAI Fundamentals Training Online Certification Course for Your Professional Growth
Strengthen your professional capabilities with practical learning, industry-relevant knowledge, and skills applicable to real-world business and technology environments.
Industry-Relevant Skills
Gain knowledge aligned with current industry practices, technologies, processes, and professional requirements.
Practical Learning
Understand concepts through practical scenarios, instructor-led discussions, exercises, and use cases.
Enhanced Professional Capability
Strengthen your ability to work confidently with relevant tools, workflows, platforms, and business processes.
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Add valuable capabilities to your profile and explore opportunities across relevant roles, projects, and industries.
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Stay familiar with evolving technologies, methodologies, and practices shaping modern enterprise environments.
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Demonstrate your training achievement with a course completion certificate from Multisoft Systems.
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AI & GenAI Fundamentals Training Online Certification Course FAQ's
The training introduces the fundamental concepts of artificial intelligence and generative AI, including machine learning, neural networks, LLMs, prompt engineering, GenAI applications, and responsible AI practices.
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