AI Task Suite — CodSoft

Inspiration

As part of the CodSoft Artificial Intelligence internship, I wanted to build a compact suite of projects that showcase different foundational AI techniques rather than just one narrow demo.

What it does

This project bundles three AI systems:

• A rule-based chatbot that uses pattern matching to respond to user queries
• A Tic-Tac-Toe AI built with the Minimax algorithm, unbeatable against optimal play
• A content-based recommendation system that suggests items using genre/feature similarity

How I built it

Each task was developed and tested in Python, using the Programiz online compiler for quick iteration. The chatbot relies on rule-based pattern matching, the Tic-Tac-Toe AI implements adversarial search via Minimax, and the recommender uses content-based filtering (comparing item features rather than collaborative user data).

Challenges I faced

Tuning the Minimax function to evaluate game states correctly, and designing chatbot rules broad enough to handle varied phrasing without becoming unwieldy.

What I learned

Hands-on experience with three core pillars of AI: rule-based systems, adversarial search, and recommendation algorithms — and how each solves a different class of problem.

What's next

Extending the recommender with collaborative filtering, and adding a simple UI for the chatbot and game.

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for AI Task Suite — CodSoft

Built With

• chatbot
• minimax-algorithm
• python
• recommendation-system