Generative AI for Complete Beginners

Generative AI for Complete Beginners, available at $54.99, has an average rating of 4.92, with 27 lectures, 5 quizzes, based on 13 reviews, and has 24 subscribers.

You will learn about Fine tuning an LLM Prompt Engineering Gen AI Basics OpenAI API Usage Langchain Basics This course is ideal for individuals who are Beginners willing to understand gen ai or Beginner willing to learn LLM or Students who want to build projects on Generative AI or Students willing to learn Langchain It is particularly useful for Beginners willing to understand gen ai or Beginner willing to learn LLM or Students who want to build projects on Generative AI or Students willing to learn Langchain.

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Summary

Title: Generative AI for Complete Beginners

Price: $54.99

Average Rating: 4.92

Number of Lectures: 27

Number of Quizzes: 5

Number of Published Lectures: 27

Number of Published Quizzes: 5

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Fine tuning an LLM

Prompt Engineering

Gen AI Basics

OpenAI API Usage

Langchain Basics

Who Should Attend

Beginners willing to understand gen ai

Beginner willing to learn LLM

Students who want to build projects on Generative AI

Students willing to learn Langchain

Target Audiences

Beginners willing to understand gen ai

Beginner willing to learn LLM

Students who want to build projects on Generative AI

Students willing to learn Langchain

This course is a practical guide to learning Generative AI concepts:

–  Large Language Models

– Tokenization, Word Vectors and Embeddings

– Fine tuning LLMs

– Langchain

– Prompt Enginnering concepts

– Using OpenAI API

The field of artificial intelligence has seen incredible advances in recent years, with one area gaining significant traction – generative AI. This cutting-edge technology is poised to revolutionize how we create and interact with all kinds of digital content.

So, What exactly is generative AI? At its core, it refers to AI models that can generate new data, rather than just analyzing existing data. This could include generating text, images, audio, video, computer code, and more – often starting from just a basic prompt or input from a user. What makes this technology so powerful is how user-friendly it is becoming. You can simply describe a scene or concept, and the AI model generates high-quality digital content in response – almost like magic!

1. Artificial Intelligence:

This prime spot is reserved for just plain AI. It’s a broad term, the overarching goal: machines that mimic human intelligence. That includes everything from playing chess to diagnosing diseases, from composing music to writing this blog.

2. Machine Learning:

Machine Learning (ML), a subset of AI. It’s where the magic of learning from data happens. ML algorithms don’t need explicit programming – they gobble up data, identify patterns, and improve their performance over time.

3. Deeper and Deeper: When ML Gets Fancy – Deep Learning

This is ML on steroids, using complex artificial neural networks loosely inspired by the human brain. Deep learning is the secret sauce behind many of AI’s recent breakthroughs, allowing for crazy-powerful stuff like image and speech recognition.

4. Generative AI

This is the elephant baby of the AI world or the rebellious teenager with a paintbrush. It uses machine learning to create entirely new content, from composing electronic dance music symphonies to generating hyperrealistic images of, well, anything you can imagine (including, unfortunately, deepfakes so convincing they’d make our grandma believe the orange cats can dance).

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is Generative AI?

Lecture 2: Resources

Lecture 3: Ven Diagram of AI x GenAI

Lecture 4: Roadmap to learn Generative AI

Lecture 5: Preparing our machine for GenAI Experiments

Lecture 6: Using OpenAI API Programmatically

Lecture 7: LLM Toolbax : Temperature

Lecture 8: LLM Toolbox: Max Token

Lecture 9: Understanding Tokenization

Lecture 10: Practical Implementaion: Character Tokenization

Lecture 11: Practical Implementation: Word Tokenization

Lecture 12: Practical Implementation: Sub Word Tokenization

Lecture 13: Word Vectors and Embeddings

Lecture 14: Practical: Word Vectorization and Embeddings

Lecture 15: Practical: Cosine Similarity

Chapter 2: LLM Fine Tuning Arc

Lecture 1: Introduction to LLM Training

Lecture 2: Metrics and Benchmarks

Lecture 3: Introduction to Fine Tuning

Lecture 4: Why Fine Tuning is needed?

Lecture 5: Data Collection for Fine Tuning

Lecture 6: Estimating the cost of Fine Tuning

Lecture 7: Formatting Data for Fine Tuning

Lecture 8: Fine tuning in Action

Lecture 9: Using custom fine-tuned model

Lecture 10: Serving fine tuned model as an API

Chapter 3: Prompt Engineering

Lecture 1: AUTOMAT Framework of Prompt Engineering

Chapter 4: Major Project: Building a PDF Chatbot

Lecture 1: Building a Naive Chatbot

Instructors

Sourabh Sinha
Team Lead at AiInterview.io

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4 stars: 1 votes

5 stars: 12 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!