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Mastering Generative Ai – Foundations To Advanced Application

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Free Download Mastering Generative Ai – Foundations To Advanced Application

Published: 2/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.54 GB | Duration: 18h 3m
Unlock the Power of Generative AI: Build, Deploy, and Innovate with Cutting-Edge AI Technologies

What you'll learn

Master Generative AI Basics: Build proficiency with frameworks like LangChain and Hugging Face, developing a strong foundation in core AI principles and tools.
Create Multimodal Apps: Learn to build AI that processes text, audio, and images, including chatbots and document-based interactions using PDFs, Excel, and SQL.
Apply Advanced Image/Text Manipulation: Use AI for image upscaling, recoloring, generative filling, and RAG to enhance and transform multimedia content.
Develop Multi-Agent Systems: Design AI systems with multi-agent collaboration, setting LLM guardrails, API management, and optimized agent configurations.
Requirements

Basic Python Knowledge: Understanding Python fundamentals is essential for working with AI scripts.
Familiarity with AI/ML Concepts: A basic understanding of large language models, data preprocessing will help in comprehending Generative AI techniques.
Description

The Generative AI Mastery: This comprehensive course is crafted for AI enthusiasts, data scientists, and professionals looking to deepen their expertise in Generative AI. Covering a wide range of AI capabilities, it guides learners through building, refining, and evaluating AI systems capable of generating, analyzing, and modifying text, images, and audio. Using cutting-edge frameworks such as LangChain, Llama Index, and Hugging Face, students will gain hands-on experience with core Generative AI techniques, including Retrieval-Augmented Generation (RAG), image classification, vector embeddings, and model fine-tuning.Throughout the course, you'll explore how to set practical guardrails, ensure model alignment, and manage multiple large language models (LLMs) within a single application. Each module combines theory with hands-on projects, helping students put their skills to work on real-world tasks. Projects include document analysis, interacting with and analyzing SQL databases via natural language, and voice cloning. These projects, along with advanced multimodal exercises, will solidify your understanding of AI's practical applications. By course completion, you'll be equipped with the skills to design, deploy, and innovate in the field of Generative AI, allowing you to harness its full potential across diverse industries and applications, and empowering you to develop impactful AI-driven solutions in today's fast-evolving tech landscape.
Overview

Section 1: Introduction to Generative AI: Building Applications with Llama Index
Lecture 1 Introduction to Generative AI.
Lecture 2 How to build AI application with frameworks. Introduction to Llama Index.
Lecture 3 How to use Generative AI to chat with PDF documents.
Section 2: Evaluating Gen AI: Tokenization, Confusion Matrix & Document Summarization
Lecture 4 How to evaluate Generative AI output using confusion matrix.
Lecture 5 Introduction to Tokenization in Generative AI.
Lecture 6 How to use Generative AI to summarize documents.
Section 3: Multimodal AI: Building Audio Apps, Function Calling & Streamlit Applications
Lecture 7 Introduction to Multimodal AI. How to build Audio based GenerativeAI application
Lecture 8 How to use Function Calling in Generative AI applications.
Lecture 9 How to use Streamlit for building Generative AI applications.
Section 4: Guardrails and Multi-Functionality in Conversational AI Applications
Lecture 10 How to set Guardrails for LLMs while building Generative AI applications.
Lecture 11 How to build conversative Generative AI applications.
Lecture 12 How to create multiple LLM functionalities inside same Generative AI application
Section 5: Integrating LLMs, Hugging Face, and Image Classification in Generative AI
Lecture 13 How to configure multiple LLM models inside same Generative AI application.
Lecture 14 How to use Huggingface Platform in building Generative AI application.
Lecture 15 How to use Image Classification techniques in Generative AI application.
Section 6: FastAPI, API Management, and RAG for Generative AI Applications
Lecture 16 How to use FastHtml for building Generative AI applications.
Lecture 17 How to manage api calls in Generative AI applications using limitation, deletion
Lecture 18 How to use Retrieval Augumented Generation for grounding LLMs.
Section 7: Optimizing Generative AI with Vector Embeddings, RAG, and Databases
Lecture 19 How to use Vector Embeddings to build effective Generative AI application.
Lecture 20 How to improve RAG applications using techniques like Reranking, Query Expansion
Lecture 21 How to use vector databases in building Generative AI applications.
Section 8: Building Image-Based Generative AI with LangChain and Vision Techniques
Lecture 22 How to use Langchain framework to build effective Generative AI application.
Lecture 23 Intro to Computer Vision & Image-Based Generative AI.
Lecture 24 How to use Generative AI to upscale image quality of low resolution images.
Section 9: Generative AI: Image Expansion, Object Replacement, and Image-to-Text
Lecture 25 How to use Generative AI to extend size of images to bigger sizes.
Lecture 26 How to use Generative AI to replace an object from a image.
Lecture 27 How to use Image-to-Text in building effective Generative AI Applications.
Section 10: Generative AI: Remove, Recolor, and Repair Images with Natural Language
Lecture 28 How to use Generative AI to remove unwanted portions of images.
Lecture 29 How to use Generative AI to change the color of object inside a image.
Lecture 30 How to use Generative AI to repair portions of image that has been torn.
Section 11: Generative AI: Chain of Thought, Excel Chat, and SQL Data Analysis
Lecture 31 Implementing Chain of Thought in Generative AI for LLM Optimization.
Lecture 32 How to use Generative AI to chat with Excel documents.
Lecture 33 How to use Generative AI to run SQL commands and perform data analysis.
Section 12: AI Agents: Overcoming LLM Limits and Deploying Multi-Agent Systems
Lecture 34 How to overcome knowledge cutoff limitations of LLMs by deploying search agents.
Lecture 35 Introduction to AI agents. How to deploy AI agents to perform various tasks.
Lecture 36 Introduction to Multi-Agent AI Systems and Deployment.
Section 13: Hierarchical AI Agents, Image-to-Video, and Voice Cloning with Generative AI
Lecture 37 Introduction to Hierarchical AI Systems: Building Agent Supervision
Lecture 38 How to use Image-to-Video Technology in building effective Gen AI Applications.
Lecture 39 How to use Generative AI to perform voice cloning and text to speech conversion.
Section 14: Fine-Tuning Models for Domain-Specific Tasks
Lecture 40 Introduction to Fine-Tuning Base Models for Domain-Specific Tasks.
Aspiring AI Developers,Data Scientists and Machine Learning Engineers,AI Enthusiasts and Hobbyists,Product Managers or Entrepreneurs,AI Researchers or Academics,Professionals Seeking Career Transition into AI
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