CodSoft AI Internship Tasks
📘 TASK 1 – CHATBOT WITH RULE-BASED RESPONSES
Task 1 – Chatbot with Rule-Based Responses
CODSOFT Artificial Intelligence Internship
This project implements a simple rule-based chatbot that responds to user inputs using predefined rules and conditional logic. The chatbot identifies keywords or patterns in user queries and returns appropriate responses.
- Understand basic chatbot design
- Learn rule-based Natural Language Processing
- Implement conversation flow using conditions
- Python
- If-Else Logic
- String Matching
Task1_Chatbot/ ├── chatbot.py └── README.md
- User enters a message
- Input is matched with predefined rules
- Chatbot returns a response based on conditions
python chatbot.py
✅ Output Example
User: Hello
Bot: Hi! How can I help you today?
📌 Conclusion
This project demonstrates how basic NLP concepts can be used to build conversational agents using rule-based logic.
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# 📘 TASK 2 – TIC TAC TOE AI
```md
# Tic Tac Toe AI using Minimax Algorithm
## 📌 Internship Task
Task 2 – Tic Tac Toe AI
CODSOFT Artificial Intelligence Internship
---
## 📖 Project Overview
This project implements an **AI-powered Tic Tac Toe game** where a human player competes against an AI. The AI uses the **Minimax algorithm** to make optimal decisions, making it unbeatable.
---
## 🎯 Objectives
- Understand game theory
- Implement Minimax algorithm
- Build decision-making AI
---
## 🛠️ Technologies Used
- Python
- Minimax Algorithm
- Game Tree Search
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## 📂 Project Structure
Task2_TicTacToe_AI/
├── tic_tac_toe.py
└── README.md
---
## ⚙️ Working Principle
- Game board is represented as a matrix
- AI evaluates all possible moves
- Minimax selects the optimal move
---
## ▶️ How to Run
```bash
python tic_tac_toe.py
✅ Result
The AI always plays optimally and never loses.
📌 Conclusion
This project demonstrates how AI decision-making algorithms can be applied to classic board games.
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# 📘 TASK 3 – IMAGE CAPTIONING (Already Updated)
```md
# Image Captioning using CNN (ResNet50)
## 📌 Internship Task
Task 3 – Image Captioning
CODSOFT Artificial Intelligence Internship
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## 📖 Project Overview
This project generates **textual descriptions for images** by combining **Computer Vision** and **Natural Language Processing**. A pre-trained **ResNet50 CNN** is used to extract image features.
---
## 🎯 Objectives
- Combine CNN and NLP concepts
- Extract visual features from images
- Generate meaningful captions
---
## 🛠️ Technologies Used
- Python
- TensorFlow / Keras
- ResNet50
- NumPy
- Pillow
---
## 📂 Project Structure
Task3_ImageCaptioning/
├── app.py
├── requirements.txt
├── README.md
└── dataset/
└── Images/
└── sample.jpg
---
## ▶️ How to Run
```bash
pip install -r requirements.txt
python app.py
✅ Sample Output
a person standing inside a room
📌 Conclusion
This project demonstrates how visual data can be translated into natural language using deep learning.