Generative AI for Complete Beginners (Udemy.com)
Learn GenAI, LLM, Langchain, Prompt Engineering in a project based approach
Created by: Sourabh Sinha
Last updated July 2024
What you will learn
- Fine tuning an LLM
- Prompt Engineering
- Gen AI Basics
- OpenAI API Usage
- Langchain Basics
How it compares
Against the other 461 rated Udemy courses in Data Science on CourseDuck:
- Rating4.4 out of 5higher than 28% of them
- Reviews1,361#117 of 462 by number of reviews
- Length2.2 hoursshorter than 88% of them
- Last updatedJuly 2024earlier than 70% of them
Course 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).
Instructor Details
- 4.4 Rating
1,361 Reviews
Sourabh Sinha
I am a Team Lead at AiInterview. I have a special interest in databases.
I started with Django, DRF. At some point, I felt that using batteries included frameworks, steals a lot of learning opportunities.
Now, I am trying to dig deeper into raw backend development, databases, linux and networking with a new friend in the town, FastAPI. I really do believe that learning things the hard way helps understand software from the core. So, I include test-driven development, basics, and best practices even if it makes my course harder to follow.
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Reviews
By Richard Jung on 9/16/2026
It is an interesting course, and I am learning the basic inerrancies of the AI process.
By Praveen Nimbekar on 6/14/2026
It is going well so far but little bit superficial. learning with practica; examples will be very helpful.
By Milind Deosthali on 6/1/2026
It would be better if you would have added examples stating what exactly LM contains, how is it created, details about tools and how they are used to create each component of this whole ecosystem.
By Arunkumar G on 5/20/2026
no examples we need real examples unlike explaination points
By Anjaneya Ravi Prasad Kambala on 4/2/2026
Really excellent series. Waiting for AI Agents course too.
By Jakub Oleksiak on 3/13/2026
Good Overview. Remark: In the Resource chapter, there is a need to use/have a google account to login to the jupyter. Can it be optimized, so no such logging is necessary?
By Rajeev Honnavally on 3/4/2026
Well explained via simple graph and connecting the dot in between
By Michael Smith on 2/12/2026
It's been a great experience thus far.
By Harsh Srivastava on 2/11/2026
It was really informative! The hands‑on examples felt very relatable to real-life scenarios and made the concepts much clearer.
By Parvinder Singh on 12/20/2025
engaging and informative that what are the pre-requisites to complete GenAI. Big thanks Sourabh.
Quality Score
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Overall Score : 88 / 100







