Course Details

AI Engineering Advanced

GenAI with Python Course

This comprehensive course takes you on a complete journey through Generative AI using Python.

Mr. S Mondal

AI Engineer

Working at Graphicodx

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Course Description

This comprehensive course takes you on a complete journey through Generative AI using Python. Starting from Python fundamentals and progressing to building production-ready AI applications, you will master the tools, frameworks, and techniques used by industry professionals today.

Whether you are a beginner stepping into AI or an experienced developer looking to specialize in Generative AI, this series provides a structured, hands-on learning path with real-world projects at every stage.

Course Modules Overview

Python Foundations for AI

Python basics, NumPy, Pandas, Matplotlib

Introduction to Generative AI

Generative AI concepts, LLMs, Prompt Engineering

Working with OpenAI API

GPT models, API setup, prompt engineering, tokens

Prompt Engineering

Zero-shot, few-shot, CoT, ReAct, system prompts

LangChain Framework

Chains, agents, memory, document loaders, tools

Vector Databases & RAG

Vector databases, RAG pipelines, embedding models

Fine-Tuning LLMs

Model fine-tuning, training data preparation, hyperparameter tuning

Image Generation

Stable Diffusion, DALL·E, image generation pipelines

Audio & Speech AI

Speech recognition, text-to-speech, audio processing

AI Agents & Automation

Autonomous agents, workflow automation, task orchestration

Building AI Applications

Application architecture, deployment strategies, scalability considerations

Production & MLOps

Model deployment, monitoring, CI/CD pipelines, infrastructure management

Requirements

Access to a Well-Functioning Computer
Stable Internet Connection
Basic Computer Proficiency
Interest in Artificial Intelligence

12 Modules

30+ Technologies & Tools Covered

10+ Practical Projects

Python data types, control flow, functions, and classes Topic-1

NumPy arrays, matrix operations, and broadcasting Topic-2

Pandas DataFrames for data manipulation and analysis Topic-3

Data visualization with Matplotlib and Seaborn Topic-4

Jupyter Notebooks and VS Code setup for AI development Topic-5

What is Generative AI and how does it differ from traditional ML? Topic-1

History of language models: RNNs, Transformers, GPT, BERT Topic-2

Overview of major GenAI models and providers (OpenAI, Google, Meta, Mistral) Topic-3

Ethical considerations, bias, hallucination, and responsible AI use Topic-4

Real-world applications: chatbots, code generation, content creation Topic-5

Setting up the OpenAI Python SDK and API key management Topic-1

Text completions and Chat Completions endpoints Topic-2

Understanding tokens, context windows, and pricing Topic-3

Building a conversational chatbot with memory Topic-4

Function calling and structured outputs Topic-5

Zero-shot, one-shot, and few-shot prompting Topic-1

Chain-of-Thought (CoT) and Tree-of-Thought prompting Topic-2

ReAct (Reasoning + Acting) framework Topic-3

System prompts, personas, and instruction tuning Topic-4

Prompt templates and dynamic prompt generation with Python Topic-5

LangChain architecture: chains, prompts, models, parsers Topic-1

Document loaders, text splitters, and embedding pipelines Topic-2

Memory systems: conversation buffer, summary, entity memory Topic-3

Building agents with tools: search, calculator, code execution Topic-4

LangGraph for stateful multi-step AI workflows Topic-5

What are embeddings and how do they represent meaning? Topic-1

Building and querying FAISS and Chroma vector stores Topic-2

Cloud vector databases: Pinecone, Weaviate, Qdrant Topic-3

Retrieval-Augmented Generation (RAG) architecture and pipeline Topic-4

Advanced RAG: reranking, hybrid search, parent-child chunking Topic-5

Transfer learning and why fine-tuning works Topic-1

Preparing and formatting datasets for instruction tuning Topic-2

Parameter-Efficient Fine-Tuning (PEFT): LoRA and QLoRA Topic-3

Using Hugging Face Transformers and the TRL library Topic-4

Evaluating fine-tuned models with benchmarks and human review Topic-5

How diffusion models work: noise, denoising, and latent space Topic-1

Stable Diffusion with the Diffusers library Topic-2

DALL-E 3 via the OpenAI API Topic-3

ControlNet, img2img, and inpainting workflows Topic-4

Building an image generation web app with Gradio Topic-5

OpenAI Whisper for speech-to-text transcription Topic-1

Text-to-speech with ElevenLabs and OpenAI TTS Topic-2

Audio processing with Librosa and FFmpeg Topic-3

Building a voice-powered AI assistant Topic-4

Music and sound generation with AudioCraft Topic-5

Agent architectures: ReAct, Plan-and-Execute, MRKL Topic-1

Tool definition and integration (APIs, databases, browsers) Topic-2

Multi-agent collaboration with CrewAI and AutoGen Topic-3

Building a research agent and a code execution agent Topic-4

Safety, guardrails, and human-in-the-loop design Topic-5

Rapid prototyping with Streamlit and Gradio Topic-1

Building production-grade REST APIs with FastAPI Topic-2

Authentication, rate limiting, and API key management Topic-3

Containerizing AI apps with Docker Topic-4

Deploying to cloud platforms: AWS, GCP, Azure, Hugging Face Spaces Topic-5

LLM evaluation frameworks: RAGAS, DeepEval, TruLens Topic-1

Observability and tracing with LangSmith and Langfuse Topic-2

Cost monitoring and token optimization strategies Topic-3

A/B testing prompts and models in production Topic-4

CI/CD pipelines for AI applications and model versioning Topic-5

4.8

Student Reviews

Jessica Chen

2 weeks ago

Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. The instructor explains complex concepts very clearly.

David Thompson

1 month ago

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Great practical examples and real-world projects that helped me understand the concepts better.

₹30,000 ₹50,000 40% OFF

Building the Future of AI Learners – Join Now

Certificate included

Offline Class Mode

Live Projects Assignment

Problem Solving on Real World Scenarios

Placement Assistance

T&C Apply

Course Details

Duration 12 Months

Skill Level Advanced

Class Lacture English, Bengali

Semester 2 Times

Assignments 10+ projects

Class Schedule Weekends or Weekdays

Course Details

GenAI with Python Course

Mr. S Mondal

Course Description

Course Modules Overview

Python Foundations for AI

Introduction to Generative AI

Working with OpenAI API

Prompt Engineering

LangChain Framework

Vector Databases & RAG

Fine-Tuning LLMs

Image Generation

Audio & Speech AI

AI Agents & Automation

Building AI Applications

Production & MLOps

Requirements

Module 01 — Python Foundations for AI A fast-paced review of Python essentials with a focus on AI and data science workflows. Covers core Python syntax, object-oriented programming, and the key scientific libraries.

Module 02 — Introduction to Generative AI Establishes a solid conceptual foundation in Generative AI, its history, and its transformative impact across industries.

Module 03 — Working with OpenAI API Hands-on experience with OpenAI's powerful API to build text, code, and chat-based applications using Python.

Module 04 — Prompt Engineering Master the art and science of crafting effective prompts to maximize the quality and reliability of LLM outputs.

Module 05 — LangChain Framework Explore the most popular framework for building LLM-powered applications, covering chains, agents, memory, and integrations.

Module 06 — Vector Databases & RAG Learn how to augment LLMs with external knowledge using embeddings and vector stores to build accurate, up-to-date AI systems.

Module 07 — Fine-Tuning LLMs Go beyond prompting by customizing pre-trained models on your own data for specialized domains and tasks.

Module 08 — Image Generation Unlock the power of visual generative AI with diffusion models and image generation APIs for creative and practical applications.

Module 09 — Audio & Speech AI Integrate speech recognition, text-to-speech, and audio generation capabilities into your AI applications.

Module 10 — AI Agents & Automation Build autonomous AI agents that can plan, reason, use tools, and complete complex multi-step tasks with minimal human input.

Module 11 — Building AI Applications Combine everything learned to build complete, user-facing AI applications with modern Python web frameworks and UI libraries.

Module 12 — Production & MLOps Learn the practices and tools required to operate AI systems reliably at scale in real-world production environments.

Jessica Chen

David Thompson

Course Details

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