Generative AI Full Course – Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More

Learn about generative models and different frameworks, investigating the production of text and visual material produced by artificial intelligence. This course was originally recorded live.

Instructors: Krish Naik, Sunny Savita, and Boktiar Ahmed Bappy.


⌨️ (00:00:00) DAY 1: Introduction to Generative AI Community Course
⌨️ (01:44:14) DAY 2: Introduction to OpenAI and understanding the OpenAI API
⌨️ (03:37:49) DAY 3: Introduction to LangChain
⌨️ (05:16:48) Day 4: Hugging Face API + Langchain
⌨️ (07:13:08) DAY 5: Memory in Langchain
⌨️ (09:07:53) DAY 6: LLM Generative AI Project using OpenAI & LangChain
⌨️ (11:03:29) DAY 7: LLM Generative AI Project & Deployment
⌨️ (13:09:02) DAY 8: Introduction to Vector Database for AI & LLM
⌨️ (14:52:41) DAY 9: Mastering Vector Databases with Pinecone
⌨️ (17:02:19) DAY 10: Mastering ChromaDB Vector Databases
⌨️ (19:04:25) DAY 11: Introducing Meta Llama 2
⌨️ (20:54:33) DAY 12: End to End Medical Chatbot Project, Part 1
⌨️ (22:36:05) DAY 13: End to End Medical Chatbot Project, Part 2
⌨️ (24:22:10) Generative AI: Everything You need to know about Gemini Pro LLM Models
⌨️ (26:16:33) End to End Gen AI Project using Google Gemini Pro
⌨️ (28:24:14) Webinar - Generative AI Revolution: The Future

🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
👾 jedi-or-sith
👾 南宮千影
👾 Agustín Kussrow
👾 Nattira Maneerat
👾 Heather Wcislo
👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan
👾 Oscar Rahnama

--

[kevinlucas]

Respect to whoever finishes this 30 hours… I’m right behind y’all I swear!

[nazarowrustem]

I start wathing when i was single....now i have wife and 2 kids and still watching

[zentootv]

wow, dropping a 30 hours course, just above and beyond..

[kd]

00:25 *🎓 Introduction to Generative AI Community Session*
02:14 *📚 Dashboard Walkthrough and Enrollment Process*
04:19 *📋 Curriculum Overview and Instructor Introduction*
07:42 *📊 Detailed Curriculum Breakdown*
16:49 *🧠 Preparing for Generative AI and LLM Introduction*
23:26 *🧠 Basics of Deep Learning*
27:52 *🤖 Neural Network Architectures Overview*
32:07 *📊 Introduction to Generative AI*
33:28 *📝 Generative AI Models and Applications*
46:12 *🧠 Overview of Text and Image Generation*
56:31 *🔄 Addressing Sequence Mapping Issues*
01:11:52 *🧠 Understanding the Transformer Architecture*
01:17:46 *🔄 Discriminative vs Generative Models*
01:25:00 *🚀 Milestones in Large Language Models (LLMs)*
01:44:02 *📚 Accessing Resources and Engaging with the Community*
01:53:42 *🚀 Overview of Course Enrollment and Dashboard Setup*
01:56:15 *📝 Agenda for Today's Session*
02:06:02 *🌐 Introduction to OpenAI: History, Goals, and Milestones*
02:17:19 *📊 Introduction to OpenAI's API*
02:28:29 *💼 Job Opportunities and Interview Insights*
02:38:19 *📊 Checking Environment and Installing Jupyter Notebook*
02:57:50 *💻 Using the OpenAI Playground and setting up API keys*
03:04:15 *🎮 Exploring the OpenAI Playground and its functionalities*
03:10:47 *🤖 Understanding chat completion API and function calling*
03:29:08 *💡 Understanding tokenization and pricing in OpenAI API.*
03:41:05 *🧩 Prompt Templating with Hugging Face*
03:44:03 *💻 Project Development Setup and Flask Integration*
04:01:10 *🖥 Calling Chat Completion API*
04:22:04 *🖥 Extracting Textual Descriptions Using API Calls*
04:42:10 *📝 Extracting Origin and Destination from Arguments*
04:43:59 *🛫 Retrieving Flight Details*
05:03:21 *💻 Introduction to Function Calling and API Integration*
05:10:05 *🤖 Utilizing Len Chain for AI Prompting*
05:34:50 *🧩 Features and Capabilities of Len Chain*
05:42:40 *🎓 Practical Implementation and Use Cases*
05:49:34 *📝 Creating Prompt Templates*
05:57:15 *🤖 Utilizing Agents for Real-time Information Extraction*
06:09:52 *🛠 API Access and Documentation*
06:13:07 *🖥 Implementing API Calls in Code*
06:15:24 *📊 Real-Time Information Retrieval with Sur API*
06:17:18 *📚 Accessing Wikipedia Information*
06:24:10 *📄 Exploring Langchain Documentation*
06:47:00 *🔗 Sequential Chaining in LLM*
06:52:10 *🧩 Utilizing Sequential Chains in AI Strategy*
07:24:41 *🛠 Len chain project implementation agenda*
08:21:35 *🤖 Setting up Hugging Face Hub and accessing models*
08:30:38 *📦 Installation and library setup for Hugging Face*
08:42:01 *📚 Understanding open-source model usage*
08:46:18 *📝 Steps to download and utilize models locally*
08:54:42 *🤖 Introduction to using LennChain for generating text*
08:58:12 *💡 Exploring different prompts and models for text generation*
09:06:32 *🚀 Announcement of upcoming sessions and resources for learning*
09:25:11 *🚀 Publishing Code to GitHub*
09:37:01 *💻 Setting up Virtual Environment and .gitignore*
09:42:00 *📋 Listing Project Requirements in requirements.txt*
09:48:43 *📦 Structuring Local Package with init.py*
09:58:41 *💻 Installing Python packages and managing dependencies*
10:06:02 *📂 Exploring package metadata and project structure*
10:10:07 *🛠 Configuring API keys and environment variables*
10:24:27 *🧩 Setting Up the Environment for LLM Integration*
10:30:24 *🔗 Creating a Prompt Template for Quiz Evaluation*
10:34:46 *🤖 Connecting Components Using LLM Chain and Sequential Chain*
10:40:25 *🛠 Loading Data and Reading Text from a File*
10:46:41 *🛠 Explaining the usage of OpenAI callback for token tracking in LlamaChain*
11:06:46 *📁 Project setup and agenda overview*
11:11:09 *🛠 Adding files to project structure*
11:28:38 *🔒 Setting Up Logging in Python*
11:41:24 *📝 Using GitHub and Neuro Lab for Project Development*
11:51:33 *🛠 Setting up Virtual Environment and Installing Requirements*
11:58:36 *💻 Setting up Project Files and Importing Dependencies*
12:24:59 *🛠 Finalizing Application Structure*
12:43:31 *🧩 Generating MCQs and Displaying Results*
13:02:00 *📊 Demo and Troubleshooting Streamlit Application*
13:04:26 *🖥 Explanation of application UI and code structure*
13:06:31 *🚀 Tomorrow's agenda: Deployment and introduction to Vector Databases*
13:12:12 *📊 Generating MCQs from text data*
13:16:22 *💻 Preparation for AWS EC2 instance deployment*
13:23:22 *🚀 Starting with Generative AI*
13:24:06 *🛠 Setting up AWS EC2 Instance*
13:37:04 *📂 Cloning and Configuring Repository*
13:40:55 *🗝 Managing Environment Variables*
13:44:44 *🚀 Running the Application*
13:49:09 *📄 Saving generated quizzes in various formats*
13:53:48 *🚀 Deployment process overview and instructions*
14:09:47 *🧮 Understanding Vectors in 2D and 3D Spaces*
14:15:08 *📊 Encoding Techniques for Data Representation*
14:19:06 *📝 Introduction to Embedding Concept*
14:32:02 *🧠 Understanding Vocabulary and One-Hot Encoding*
14:47:39 *📊 Introduction to Vector Databases and Practical Implementation*
14:53:41 *📚 Enrollment Process and Dashboard Overview*
15:00:41 *💻 Accessing Additional Resources and Video Recordings*
15:20:15 *📉 Challenges with Traditional Databases*
15:38:08 *🗃 Use Cases of Vector Databases*
15:57:27 *🖥 Setting up code sharing and library installation*
16:00:56 *📁 Creating and uploading folders/files, troubleshooting file size*
16:06:52 *📑 Loading PDF data, text chunking, and preprocessing*
16:19:34 *📐 Extracting Embeddings with OpenAI*
16:29:37 *💻 Exploring Pinecone Features and Pricing*
16:42:28 *🧬 Implementing Similarity Search with Vector Databases*
17:11:45 *📝 Course Curriculum Overview*
17:19:32 *💻 Setting Up and Exploring Chroma DB in Jupyter Notebook*
17:24:31 *🔍 Overview of Chroma DB on GitHub*
17:26:35 *📚 Exploring Chroma DB Documentation*
17:47:23 *📊 Generating Embeddings and Using OpenAI Model*
17:51:27 *🗝 Setting Up OpenAI API Key*
17:55:06 *📚 Importing Libraries and Loading Data*
18:09:37 *📚 Chunking Data for Large Text Input*
18:22:12 *💽 Creating Embeddings and Storing Locally*
18:28:38 *🔄 Retriever Creation and Document Retrieval*
18:51:27 *🛠 Overview of project flow and data handling:*
19:08:27 *📊 Understanding Model Costs*
19:16:20 *🛠 Requirements for Open Source Models*
19:27:11 *🧠 Understanding Llama 2*
19:38:00 *🦙 Using the Llama 2 Model for Chat*
20:21:19 *📝 Generating responses using LLM model*
20:27:14 *🛠 Setting up LLM model with Hugging Face pipeline*
20:46:21 *🛠 Using Prompt Templates and LLM Chain*
21:12:12 *🛠 Architecture Overview*
21:18:24 *🧩 Technology Stack*
21:55:15 *📚 Setting Up Pinecone for Vector Database*
21:59:30 *📚 Loading PDF Data and Creating Text Chunks*
22:20:19 *📚 Storing Text Embeddings in Pinecone Vector Database*
22:39:15 *📂 Setting Up Project Structure*
23:16:21 *🗂 Setting Up Environment Variables and Code Structure*
23:19:13 *📝 Converting Notebook Code to Modular Components*
23:22:02 *📦 Integrating Third-Party Libraries and Dependencies*
23:24:26 *🛠 Storing Data in a Vector Database*
23:42:54 *🌐 Developing Frontend Components with Flask*
23:44:20 *📦 Setting up environment and dependencies*
23:50:36 *🌐 Creating web interface and user interaction*
24:13:51 *🎓 Information about Generative AI Course*
24:36:59 *🤖 Comparison Between Google B and Gemini*
24:13:51 *🎓 Information about Generative AI Course*
24:22:13 *📑 Agenda for Google Gemini LLM Session*
24:29:41 *🔍 Understanding Google Gemini LLM Model*
24:36:59 *🤖 Comparison Between Google B and Gemini*
25:03:58 *🧾 Automating Invoice Data Extraction with LLM*
25:07:01 *🛠 Setting up Environment and Installing Dependencies*
25:08:52 *📝 Planning and Architecture for Invoice Extraction Application*
25:25:12 *🚀 Initializing Streamlit App and User Interaction*
25:29:36 *🖥 Discussing Input Information for Response Generation*
25:39:39 *🎓 Conclusion and Course Promotion*
26:02:22 *🎓 Exploring Course Documentation*
26:03:09 *🤝 Appreciation and Course Selection*
26:05:52 *📚 Guidance for Learning Paths*
26:11:37 *💼 Career Path Discussion*
26:13:52 *🌍 Appreciation and Enrollment Queries*
26:42:55 *📊 Executing SQLite insertions and displaying records*
26:46:28 *🧠 Configuring and loading generative AI model for query generation*
26:50:09 *🗃 Creating functions for retrieving queries from the database*
26:53:10 *🚀 Setting up Streamlit app and defining prompts*
26:59:13 *💻 Setting Up Streamlit App for SQL Query Processing*
27:24:17 *🛠 Debugging SQL Query Execution*
27:36:18 *🗃 Advanced SQL queries and nested conditions*
27:46:30 *💬 Q&A Session and Course Information*
27:59:21 *🏢 Discussion on industry experience and project-based learning*
28:21:32 *📚 Overview of Future Capabilities*
28:43:00 *🧠 Understanding Generative AI*
28:49:06 *🎨 Generative Models vs. Discriminative Models*
28:57:46 *💡 Training LLM Models*
29:12:23 *⚡ GPU Infrastructure Costs and Carbon Emissions*
29:15:35 *📊 Training Metrics and Considerations*

Made with HARPA AI

[raulquindosmorales]

This is amazing, 30 hours of free knowledge right at our fingertips. Thank you so much everyone, you are truly making an impact in people’s lives.

[zayinzayin-rixl]

Have never seen such a MEGA tutorial session.
We're forever blessed with such kind hearts. Glorious blessings to you guys.

[shahzoorkhan]

Completed a 31-hour course - turns out, I can binge-watch educational content too! Who needs Netflix when you've got knowledge to gain?

[hcotechconsulting]

Krish Naik is the MAN, thanks for posting!

[chrisgriffin]

This is very generous of these two brothers to share such world knowledge of the now in later

This is gold! Thank for all the instructors❤

[faithful]

Woah. Man just needs time. 30 hrs. Well done guys.

[Mohammedamer-tjev]

they've started this course 1 or 2 months ago and completed long back on their channel, code camp collaborated with them and uploaded their complete course 2 hours ago. I really appreciate the great content. Thanks all

[zimnelredoran]

Beyond awesome, thanks a lot, 30 hour course makes a difference!

[ApuVisp]

Sunny is a great instructor. Well done and thank you

[aliberspilberg]

Me parece perfecto! mientras mas dure es mejor... Disfruto los videos largos y educativos... Espero finalizar este curso de manera correcta. Gracias...

[kingwinner-yuyb]

perfect, 00:25 Introduction to Generative AI Community Session
02:14 Dashboard Walkthrough and Enrollment Process
04:19 Curriculum Overview and Instructor Introduction
07:42 Detailed Curriculum Breakdown
16:49 Preparing for Generative AI and LLM Introduction
23:26 Basics of Deep Learning
27:52 Neural Network Architectures Overview
32:07 Introduction to Generative AI
33:28 Generative AI Models and Applications
46:12 Overview of Text and Image Generation
56:31 Addressing Sequence Mapping Issues

[RohanVetale]

The best course so far on Generative AI for free. I have just started with this one the day it was uploaded. Kudos to the whole team!!

[hunterval]

this site is gem, posting valuable things for no cost. My best wishes to all the team members

[NakedSageAstrology]

I was just about to start my education into this adventure and saw this video. The marketing is probably subliminal, but I'm excited either way!

[jinhe]

I am watching for 1.5 hour and I have to say this is an amazing resource. Thank you