Zero To Generative Ai Application Development Mastery

Master GenAI and Large Language Models from Scratch with Step by Step approach and Practical Hands-on based learning

What you'll learn

Understand the Fundamentals of Generative AI

Set Up and Configure a Generative AI Development Environment

Explore and Utilize Pre-trained Generative Models

Integrate Generative AI Capabilities into Real-world Applications

Understand about Large Language Models

Run Large Language Models locally or on servers

You will get learning materials

You will get the source code

Requirements

Basics of Python Programming Language

Description

Master Generative AI from Zero to Fullstack Generative AI Application Development – Your Ultimate Learning Path!Step into the world of artificial intelligence with our comprehensive course, “Zero to Generative AI Application Development Mastery.” Whether you're a beginner or looking to elevate your skills, this program guides you through every critical stage of building real-world AI applications.We begin with the foundations of AI and Machine Learning—understanding what AI really is, how it connects with machine learning, deep learning, and large language models (LLMs). Learn about supervised, unsupervised, and reinforcement learning and how they form the basis for today’s cutting-edge AI systems.Next, dive into the core concepts of Generative AI and LLMs. You’ll explore what makes models like ChatGPT, Claude, and Gemini tick—from tokens, context windows, and embeddings to chunks and model parameters. Discover how these elements power applications that can generate text, answer questions, and much more.In the hands-on environment setup section, we guide you through installing Python and integrating with GenAI tools such as ChatGPT, DeepSeek, and Grok. Learn different methods to interact with LLMs and gain practical experience through live coding.Then, move on to building custom applications using OpenAI, Google Gemini, and DeepSeek APIs. Learn to connect, query, and retrieve responses from LLMs directly into your apps using powerful SDKs.Explore hosting options and pricing models to deploy your own LLMs in the cloud. You’ll even use cloud-hosted models for text-to-speech, OCR, and computer vision applications.Ready to go local? We show you how to run LLMs on your own computer and call them from your code—no cloud dependency needed.Finally, cap it off with real-world fullstack app development projects that integrate multiple LLMs to solve practical problems.This is not just a course—it's your gateway to becoming a confident and capable Generative AI developer. Are you ready to master the future?

Overview

Section 1: Basics of Artificial Intelligence and Machine Learning

Lecture 1 What is Artificial Intelligence

Lecture 2 What is Machine Learning

Lecture 3 Relationship between AL ML DL LLM

Lecture 4 Supervised and Unsupervised Learning

Lecture 5 Reinforcement learning and LLM

Section 2: Fundamentals of Generative AI and Large Language Models

Lecture 6 What is Generative AI

Lecture 7 What is Large Language Model

Lecture 8 Different types of LLMs

Lecture 9 Parameters , Tokens, Context window in LLMs

Lecture 10 Chunks in LLM

Lecture 11 Embeddings in LLM

Section 3: Environment Setup and Popular GenAI Assitants

Lecture 12 Python local development environment setup

Lecture 13 What is LLM and ChatGPT with Practical Handson

Lecture 14 DeepSeek, Google Gemini, Claude, Grok

Lecture 15 Different ways to Interact with any LLM models

Section 4: Using LLMs from Custom Application via API and SDK

Lecture 16 Exploring OpenAI Platform for API Key and Docs

Lecture 17 Calling OpenAI API via Python Code Practical Handson

Lecture 18 Calling Google Gemini API via Python Code Practical Handson

Lecture 19 Calling DeepSeek API via Python Code Practical Handson

Section 5: Hosted Options for LLMs and their Pricing

Lecture 20 Hosting options for LLM models

Lecture 21 Hosting LLAMA models on Cloud and calling it via Code

Lecture 22 Using cloud hosted LLM Model for text to speech application

Lecture 23 Significance of Temparature parameter in LLM

Lecture 24 Using cloud hosted LLM for OCR and Vision capability

Section 6: Running LLMs on Local Computer

Lecture 25 How to run LLM on Local Computer

Lecture 26 Installing LLM Model on Local CPU based computer

Lecture 27 Calling locally installed LLM models from code

Section 7: Fullstack App Development with multiple LLMs

Lecture 28 Real world fullstack LLM applications

Anyone who wantes to get started with the world of Generative AI and Large Language Models,Anyone who wantes to integrate Generative AI capability in their existing projects and application