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ITRidge Global Pvt Ltd · Hyderabad, Telangana, India

artificial intelligence online trainer

seniorfull timePosted 8 days ago
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Stack mentioned

artificial-intelligencegenerative-aiagentic-aipythondata-structuresnumpydata-analysispandasdata-visualizationstatisticsllmvector-databasesragtransformersopenaicopilothugging-faceprompt-engineeringlangchainfine-tuning

Generative AI & Agentic AI – Program Contents

Course Curriculum

Python Programming

• Introduction to Python Programming

• Variables & Data Types

• Operators

• Data Structures

• Functions

• Conditional Flow statements

• Lambda, Map, Filter & Reduce functions

• Types of Errors & Error Handling

Python modules for AI

• Mathematical Computing using NumPy

• creating 1d, 2d and 3d arrays

• Accessing Array Elements

• Indexing, Slicing, Iteration

• Data Analysis using Pandas

• Understanding Pandas

• Series & DataFrames

• DataFrame Operations

• Handling Missing data

• Filtering, Grouping and Joining

• Loading Data from Datasets to DataFrames

• Data Visualization using Matplotlib & Seaborn

• Different types of Plots & Charts

• Descriptive Statistics & Inferential Statistics

• Data & Variables in Statistics

• Measures of Central Tendency (Mean, Median & Mode)

• Variance & Standard Deviation

• Univariate Analysis (Histograms, Box plots & Bar charts)

• Bivariate/Multivariate Analysis (Line plots, Scatter plots, Heat maps)

Module 1 Python for AI/ML Module 2 Statistics & EDA

• Probability Distribution & Central Limit Theorem

• Normal distribution & Standard Normal distribution

• Outlier detection

• Confidence Intervals & Hypothesis Testing

• Correlation & Regression.

Capstone Projects:

• Retail Data Insights

• Food Hub Aggregator EDA

• Introduction to AI

• Introduction to Generative AI

• Generative AI Evolution

• Traditional AI vs Generative AI

• Types of GenAI models

• GenAI vs AI agents vs Agentic AI

• Real-world applications of Gen AI

• Popular GenAI tools

• Large language models (LLMs)

• Vector database

• RAG

• Introduction to LLMs

• LLMs in the AI landscape

• What can a Language Model do

• Traditional models vs LLM

• Popular LLMs: GPT, BERT, Claude, LLaMA

• LLMs Architecture: Transformers

• Encoder & Decoder

• Tokenization

• Vector Embeddings

• Positional Encoding

• Self-Attention

• Working with ChatGPT, Copilot, Hugging Face LLMs

Module 3 AI & Generative AI Fundamentals Module 4 LLMs and Transformers

• Introduction to Prompt Engineering

• Types of prompts

• Few-shot and zero-shot prompts

• Chain-of-thought prompts

• Designing effective prompts: CRAFT framework

• Evaluating prompt performance and bias

• Prompt optimization tools and techniques

• Hands-on: zero-shot, one-shot, and few-shot prompts

• Introduction to Langchain

• Langchain ecosystem

• Langchain installation/setup

• LLMs using Langchain

• Prompt Templates and Chains

• Output Parser

• Hands-on Langchain scenarios

• Introduction to Vector databases

• Traditional databases vs Vector databases

• Differences in data storage and retrieval

• Indexing and similarity search

• High dimensional vector space

• Chromadb vector database

• Working with Chromadb

• Chromadb operations

• Distance metrics: cosine similarity, dot product, Euclidean

• Add, update, delete, query operations

• Metadata filtering

Module 5 Prompt Engineering Module 6 LangChain

Module 7 Vector Database

• How RAG works

• RAG vs traditional LLM generation

• Two-phase Architecture: Retrieval + Generation

• Document loaders

• Text splitters

• Retrieval & Answer generation

• Streamlit UI

• Use of Embedding models

• Building a RAG based pipeline

• Introduction to AI agents

• Single-agent vs. multi-agent systems

• Introduction Agentic AI

• How Agentic AI works

• AI Agents vs Agentic AI

• Real-world applications

• Building your first Agent using Llama & Agno

• Reasoning models

• Building Reasoning model using Agno

• Multi-modal Agents (text, image, video)

• Other frameworks: Google ADK, Smol agents

• Multi-Agent systems and Design patterns

• Building Multi-Agent systems

• Route agent

• Introduction to MCP

• Prebuilt MCP Servers

• A2A Protocol

• Build your first MCP Server

Module 8 Retrieval Augmented Generation (RAG) Module 9 AI Agents and Agentic AI

Module 10 Model Context Protocol (MCP)

• Introduction to Agentic AI Evaluation

• Functional Evaluation using Agno

• Safety and Guardrails

• Operational Metrics

• Performance evaluation using Agno

• Fine-Tuning basics

• Low Rank Adaptation (LoRA)Quantization (QLoRA)

• Fine tuning Llama with Unsloth

• Build AI Chat Agent

• Creation Content with N8N AI Agent

• Marketing Automation with N8N

• Introduction to Crew AI

• Building Hierarchical Agent structures

• Building Multi-Agent systems

• Building Gen AI models using Azure OpenAI

• Building Chat applications using Open AI GPT models

• Building images using Dall E model

Module 11 Agentic AI – Evaluation & Fine Tuning Module 12 AI Workflows using N8N

Module 13 Multi-Agents with Crew AI Module 14 Generative AI on Azure Cloud

Hands-On Labs & Agentic Scenarios

• Building Custom GPTs using ChatGPT

• Creating & working with SQL Data Analyst GPT

• Creating Resume Improver GPT

• AI Email Generator

• AI Blog Generator with OpenAI SDK

• Text Summarizer with Copilot

• AI Language Translator

• Code Explainer [with ChatGPT & Copilot]

• Spam Detection with Hugging Face

• Context window, Temperature

• LLM Hallucinations

• Langchain installation

• Groq and Ollama setup

• Prompting with Langchain

• Calling LLMs from Langchain

• Prompt Templates and Chains

• Output Parser

• Chromadb installation/set up

• Chromadb operations

• Add, Update, Delete and Query

• working with Metadata Filtering

• Building your first agent with LLama & Agno

• Building Reasoning Agents with Agno

• Multimodal Agents

• Building MCP Server

• Building Multi-Agent system

• LLM evaluation and fine-tuning

Capstone Real World Projects

Project-1: Creating SQL Data Analysis Custom GPT using ChatGPT Project-2: Training Deck using NotebookLM and Gamma AI

Project-3: Property buying decision using ChatGPT, Gemini, Preplexity Project-4: Real Estate RAG Agent

Project-5: E-Commerce RAG Project-6: AI chatbot using N8N

Project-7: Invoice automation using N8N Project-8: Multi-Agentic system

Project-9: Chat Agent with Azure OpenAI Projetc-10: AI agent with Microsoft Phi SLM

Project-11: Design Website Landing Page using Lovable AI Project-12: Project Tracker Automation with Notion & Zapier Project-13: Retail Data Insights using Pandas & Matplotlib Project-14: Uber Eats EDA using Pandas, Statistics & Seaborn