Retrieval Augmented Generation (RAG)

1.1: Introduction: AI for Everyone

7.1: Quiz Mandatory

Dhaval Patel

Data Entrepreneur | YouTuber | Ex - Bloomberg, Nvidia

I have 17 years of experience in Programming and Data Science working for big tech companies like NVIDIA and Bloomberg. I also run a famous YouTube channel called Codebasics where I pursue my passion for teaching. Codebasics is one of the top channels on YouTube when it comes to data science, machine learning, data structures, etc. I firmly believe that “Anyone can code” and I use analogies, simple explanations, and step-by-step storytelling to explain difficult concepts in such a way that even a high school student can understand them easily.

Hemanand Vadivel

Ex- Data Analytics Manager, 8+ Years in Europe, Microsoft Certified, Certified Supply Chain Professional

I’m a mechanical engineer who transitioned to a full time Data & Analytics manager in the UK & Germany by teaching myself Power BI, excel & anything else that was required to solve the problem. I have worked with complex data across Supply Chain, Sales, Marketing, Revenue Management, Finance and HR functions over the last 8 years to deliver effective solutions. To me, Analytics is an extra sensory organ that anyone can develop to become a superhuman at work. I have conducted several workshops and internal training to help non-technical people to become superhuman at work and now, really excited for this opportunity to share all that knowledge with you in this course.

Retrieval Augmented Generation (RAG)

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Contents

1: Welcome to AI Experience

1.1: Introduction: AI for Everyone

2: Demystify AI: Deep Learning

2.1: What is Deep Learning

2.2: Deep Learning vs Statistical Machine Learning: When to use What?

2.3: Credits Scoring to ChatGPT: Overview of Neural Network Architectures

2.4: Tooling for Deep Learning

2.5: Bank Employee To AI Engineer: Transition Story

2.6: Takeaways & Jargons

2.7: Quiz

3: Demystify AI: Gen AI, LLMs and NLP

3.1: What is Generative AI (or Gen AI)?

3.2: Evolution of Gen AI Models

3.3: What is LLM? Analogy Based Simple Explanation

3.4: What is NLP?

3.5: Embeddings and Vector Database

3.6: Retrieval Augmented Generation (RAG)

3.7: Prompt Engineering

3.8: Tooling for Gen AI, LLM

3.9: Takeaways & Jargons

3.10: Quiz

4: Data & AI

4.1: The Role of Data in AI

4.2: Data Infrastructure in a Company

4.3: Data Collection: Overview

4.4: Data Storage and Transformation: Overview

4.5: Data Distribution: Privacy, Ethics & Governance

4.6: Takeaways & Jargons

4.7: Quiz

5: 6 Industry Case Studies

5.1: Text Classification: Support Ticket Prioritization

5.2: Image Classification: Crop Yield Detection New

5.3: RAG-Based Gen AI: ChatGPT for Private Organizational Data

5.4: Chatbot: Food Delivery Chatbot

5.5: LLM Powered Real Estate Chatbot New

5.6: Recommendation System: Book Recommendations

6: AI Career Role, Skills and Salaries

6.1: AI Career Roles: Technical and Non-Technical

6.2: Which AI Career is the Best For You? AI Career Role Selection Guide

7: Final Quiz Mandatory

7.1: Quiz Mandatory

8: Supplementary Learning (Industry Projects)

8.1: Intro: How to use this supplementary learning

8.2: Statistical ML Project: Build a House Prediction System

8.3: Deep Learning Project: Build a Potato Disease Classification System

8.4: NLP Project: Build a chatbot using Dialog Flow

8.5: Gen AI Project: Build a Q & A system in retail domain

8.6: Gen AI Project: Build a news research tool in finance domain

Retrieval Augmented Generation (RAG)

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