AI Training & Certification

1. What is an Application?

2. Types of Applications

3. Web Application Fundamentals

4. Web Technologies: (List key technologies and their roles)

Frontend: HTML, CSS, JavaScript, React

Backend: Python, Java, Node.js

Databases: SQL (MySQL, PostgreSQL), NoSQL (MongoDB).

5. Software Development Life Cycle (SDLC)

Phases: Planning, Analysis, Design, Implementation (Coding), Testing, Deployment, Maintenance.

6. Application Development Methodologies

Agile: Core principles, Scrum, Kanban

Waterfall

1. What is Data

2. Types of Data

3. Data Storage

4. Data Analysis

5. Data Engineering

6. Data Science

1. The Importance of Computing Power

2. Key Computing Technologies:

CPU (Central Processing Unit)

GPU (Graphics Processing Unit)

3. Cloud Computing:

What is the Cloud?

Cloud Service Models:

IaaS (Infrastructure as a Service)

PaaS (Platform as a Service)

SaaS (Software as a Service)

1. What is Artificial Intelligence (AI)?

2. How AI Works?

3. Key Concepts:

Machine Learning (ML)

Deep Learning (DL)

4. Generative AI:

What is Generative AI?

Examples: Large Language Models (LLMs), image generation models.

5. AI in Everyday Learning

1. Customer Relationship Management (CRM)

2. Human Resource Management Systems (HRMS)

3. Retail & E-Commerce

4. Healthcare

1. Introduction to Business Analysis

Definition and Role of Business Analysts

Business Analysis Frameworks and Certifications

2. Understanding Requirements

Types of Requirements

Elicitation Techniques and Documenting Requirements

Prioritizing and Managing Requirements Changes

3. Business Analysis Tools and Techniques

Overview of tools used in business analysis for various purposes, including elicitation, documentation, and validation.

1. Lifecycle Management

Overview of the Business Analysis Life Cycle

Comparison with Project Management Life Cycle

Planning, Monitoring, and Evaluating Solutions

2. Working with Stakeholders

Stakeholder Identification and Analysis

Communication Planning and Effective Meeting Facilitation

Building Relationships and Negotiating Conflicts

1. Conducting Discovery

Assessing the Business Environment and Needs

Identifying Problems, Opportunities, and Conducting Feasibility Studies

Developing Business Cases

2. Process Mapping and Improvement

Business Process Modeling (BPMN) and Mapping Techniques

Identifying Process Inputs, Outputs, and Actors

Analyzing and Improving Business Processes with Metrics

1. Designing Solutions

Principles of Solution Design and Design Thinking

User Interface Design and Prototyping Techniques

Usability Testing and Iterative Design for Solutions

2. Requirements Validation

Techniques for Validating Requirements

Tracing Requirements and Ensuring Alignment with Business Needs

1. Software Testing

Fundamentals of Testing in Business Analysis

Planning, Design, Execution, and Automation of Tests

Performance Testing and Managing Defects

2. Release Management and Post-Implementation

Planning and Managing Releases

Deployment Execution and Post-Go-Live Support

Ensuring Business Continuity and Fostering Continuous Improvement

Introduction to Generative AI

1. What is Generative AI?

2. Key Applications:

Text (ChatGPT, Claude, LLaMA)

Images (DALL·E, MidJourney, Stable Diffusion)

Audio (Music Generation, Voice Cloning)

Code (GitHub Copilot, Cursor)

3. Evolution of GenAI:

Rule-Based → Deep Learning → Transformers

GANs vs. VAEs vs. LLMs

1. Effective Prompt Design

Instruction-Based, Few-Shot, Zero-Shot

2. Advanced Techniques:

Chain-of-Thought (CoT) Prompting

Self-Consistency & Iterative Refinement

Hands-on:

Transformer Architecture

1. Why Transformers? (Limitations of RNNs/LSTMs)

2. Key Components:

Self-Attention & Multi-Head Attention

Encoder-Decoder (BERT vs. GPT)

3. Evolution: BERT → GPT → T5 → Mixture of Experts

4. Large Language Models (LLMs)

5. Pre-training vs. Fine-tuning

6. Popular Architectures:

GPT-4, Claude, Gemini, LLaMA 3

BERT (Encoder-based) vs. T5 (Text-to-Text

Introduction to AI Agents

1. What are AI Agents?

2. vs. Traditional AI:

3. Applications:

AI Agent Frameworks

1. CrewAI (Multi-Agent Collaboration):

2. n8n (Workflow Automation):

Designing AI Agents

CrewAI + n8n: Automating Business Workflows

Multi-Agent Systems: Collaboration & Specialization

Real-World Applications

Case Studies:

AI Customer Support Agents