Embeddings and Vector Database

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.

Embeddings and Vector Database

Lecture content Locked

Enroll in Bootcamp to Unlock

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

Embeddings and Vector Database

Lecture content Locked