Generative AI Full Course 2025 (Learn to Build LLM From Scratch) | Generative AI Course |Intellipaat

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Published on March 18, 2025 8:27:25 PM ● Video Link: https://www.youtube.com/watch?v=95G5j2FW5aM

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Are you ready to dive into the world of Generative AI? This Generative AI full course by Intellipaat will take you through the fundamentals and advanced concepts of Generative AI, LLMs, and transformers. Whether you're a beginner or an AI enthusiast looking to master Generative AI training, this course is designed to help you build an LLM from scratch. You'll explore key concepts like Hugging Face tutorials, LangChain crash courses, RAG (Retrieval-Augmented Generation) in Generative AI, and the BERT model explained in-depth.

This Generative AI complete course covers everything from Generative AI for beginners to advanced LLM training with practical, hands-on projects. If you've ever wanted to learn about AI model building, transformer architectures, or Hugging Face full course tutorials, this is the perfect resource. Throughout this course, you'll gain expertise in LLMs, generative AI tutorials with Python, and AI implementation strategies.

📖 Below are the concepts covered in the video on 'Generative AI Full Course 2025 (Learn to Build LLM From Scratch)':

00:00:00 - Introduction to Generative AI Full Course
00:03:49 - What is Generative AI?
00:10:06 - What is LSTM?
00:14:38 - Putting This All Together
00:24:19 - RNN Model Hands-on
00:37:17 - Why Are Transformers Better Than LSTM?
00:37:56 - High-Level Overview
00:39:11 - Breaking the Black Box
00:40:28 - Expanding the Encoders and Decoders Block Further
00:41:52 - Self-Attention
00:44:52 - Self-Attention in Detail
00:53:51 - The Bigger Picture
01:02:24 - What is BERT?
01:05:10 - How BERT Works?
01:10:26 - BERT Uses Unsupervised Training Approach
01:16:17 - Hugging Face Small Example
01:21:22 - Transformer Hands-on
01:28:12 - BERT Uses Next Sentence Prediction (NSP)
01:30:33 - BERT Size and Architecture
01:41:56 - BERT Working
01:42:45 - Hands-on: Import Transformer
01:45:56 - Tokenizer Model Hands-on
02:07:55 - Binary Classification Using Logistic Regression
02:13:32 - Overview of Transformers
02:15:40 - Transformer Encoder and Decoder
02:18:36 - The Encoder Workflow
02:21:11 - Step 1 - Input Embeddings
02:22:43 - Step 2 - Positional Encoding
02:26:58 - Step 3 - Stack of Encoder Layers
02:30:20 - Step 3.1 - Multi-Headed Self-Attention Mechanism
02:36:56 - Step 3.2 - Normalization and Residual Connection
02:41:11 - The Decoder Workflow
02:44:34 - Step 3.1 - Masked Self-Attention Mechanism
02:45:40 - Step 3.2 - Encoder-Decoder Multi-Head Attention (Cross-Attention)
02:46:47 - Output of the Decoder
03:00:13 - Transition to Transformer
03:26:53 - What is Hugging Face?
03:32:27 - Core of Hugging Face
03:35:05 - Models
03:37:24 - Datasets
03:39:15 - Spaces
03:40:32 - Open Source Libraries and Documentation
03:44:55 - Hugging Face Accounts and Tokens
04:08:33 - Hands-on - Hugging Face
04:49:16 - LLM Evolutionary Tree
05:06:51 - What is Generative AI?
05:18:14 - How to Use/Access Generative AI Models?
05:47:26 - OpenAI
06:00:22 - How to Access Models Like These
06:21:40 - LangChain Model
06:57:40 - What is the POTUS?
07:06:13 - Prompts
07:10:45 - Hands-on - LLM
08:16:31 - Retrieval Augmented Generation
08:24:38 - RAG Architecture
09:01:42 - Vector Database
09:28:11 - Prompting Techniques
10:58:32 - Core AI Workflow

📌 FAQs on Generative AI Full Course

Q1: What is Generative AI and why should I learn it?
Generative AI is a cutting-edge technology that enables machines to generate human-like text, images, and even code. Learning it through a Generative AI course will give you the skills to work with LLMs, transformers, and AI-powered applications.

Q2: Can I learn Generative AI without coding experience?
Absolutely! This Generative AI full course for beginners starts from the basics and gradually builds up to advanced concepts like BERT model explained, Hugging Face full course, and RAG Generative AI techniques.

➡ ️ About the Course
This Generative AI and Prompt Engineering course helps you master AI language, design effective prompts, mitigate biases, and enhance AI interactions. By the end, you'll craft better prompts for more accurate Gen AI results.

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