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Generative AI for Complete Beginners
Rating: 4.4 out of 5(989 ratings)
3,323 students

Generative AI for Complete Beginners

Learn GenAI, LLM, Langchain, Prompt Engineering in a project based approach
Created bySourabh Sinha
Last updated 7/2024
English

What you'll learn

  • Fine tuning an LLM
  • Prompt Engineering
  • Gen AI Basics
  • OpenAI API Usage
  • Langchain Basics

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

See a demo
Image of coding exercise example

Course content

4 sections • 27 lectures • 2h 13m total length

  • What is Generative AI?3:22

    Generative AI refers to models that create new data from prompts. It can generate text, images, audio, video, and code, enabling intuitive content creation.

  • Resources0:01
  • Ven Diagram of AI x GenAI1:56
  • Roadmap to learn Generative AI10:21

    Navigate the roadmap to generative ai by mastering Python fundamentals, ml and dl concepts, nlp, llms, and deployment, plus vector databases, apis, and cloud ecosystems for practical product-ready ai.

  • Preparing our machine for GenAI Experiments7:55
  • Using OpenAI API Programmatically9:39
  • LLM Toolbax : Temperature2:03
  • LLM Toolbox: Max Token1:11

    Explore max tokens as a tool to restrict generation length for token saving or cost control, demonstrated by setting max tokens to five and noting the finish reason becomes length.

  • Understanding Tokenization9:18
  • Practical Implementaion: Character Tokenization1:20

    Learn to implement character tokenization by converting a string into a list of characters, producing tokens as text, and test the simple implementation.

  • Practical Implementation: Word Tokenization1:29
  • Practical Implementation: Sub Word Tokenization2:09

    Explore subword tokenization that breaks rare words into meaningful subword units such as character engrams and byte pair encodings, improving handling of misspellings and out-of-vocabulary words.

  • Word Vectors and Embeddings3:35

    Explore how tokenization, vectorization, and embeddings transform raw text into meaningful word vectors, and learn how cosine similarity reveals relationships among words like dog, wolf, and fish.

  • Practical: Word Vectorization and Embeddings4:56
  • Practical: Cosine Similarity3:12
  • Basics of Generative AI
  • Experimentation with OpenAI API

Requirements

  • Python Basics e.g. OOP
  • Knowledge of RestAPI would be useful

Description

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).

Who this course is for:

  • 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

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