This repository contains all my documented learnings as I learnt about Gen AI. Please feel free to suggest any changes / updates or add new knowledge so this helps as much people as possible.
NOTE : I am trying to keep everything as simple as possible...
Folders and their contents:
- AI Agents :- All CrewAI Agents that I have created as a part of learning to build AI Agents.
- Agentic Project :- A RAG Project for test case generator.
- Computer Vision :- All notes from the Udacity Course for Computer Vision.
- Core Concepts :- Python Core Concepts related to AI implemened on Jupyter Notebook. Blogs are posted on hashnode. Link is pasted at the end of this Readme file.
- CrewAI :- Documented learnings ; topic CrewAI
- Gen AI :- Course notes from Google Prompt Engineering Course with Vertex AI.
- Harness Engineering with Claude :- 1 of 4 courses, part of Nano-degree program "AI Engineering with Claude" on Udacity. Has all notes and code part.
- LLM & RAG :- All notes and exercises from the Udacity Course for LLM and RAG, covering the basics.
- Natural Language Processing :- All notes and exercises from the Udacity Course for Natural Language Processing.
- Practice Lab 1 :- For the introductory purpose, make a machine learning model which is supposed to predict who survived during the titanic shipwreck. (College Assignment)
- Lab 1 :- Build your first Neural Network to predict house prices with Keras. (College Assignment)
- Lab 2 :- The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset. Use Tensorflow to predict the pixelated alphabet and show its accuracy. (College Assignment)
- Lab 3 :- Implement basic logic gates using Hebbnet neural networks. (College Assignment)
- Lab 4 :- Implement Zipf's Law of Length, Law of Meanings and Heap Law. (College Assignment)
- Lab 5 :- Text classification for Sentimental analysis using KNN. Note: Use twitter data. (College Assignment)
- Lab 6 :- Case Study :- Sentiment Analysis via Reviews. (College Assignment)
- The Project :- A big agentic capstone project. (RAG + MCP + AI Agents) ; ongoing.....
- agents :- a folder for the LangGraph project files - the leave management project ; subject to improvement
Link to the blog where I document more such notes : https://learnaimldswithsk.hashnode.dev/