Introduction

• Overview of Python
• Installation & Environment Setup
• Writing and Running Python Programs
• Syntax and Indentation
• Variables and Data Types

Python Basics

• Input/Output Operations
• Control Statements (if-else, loops)
• Functions and Lambda Functions
• Error and Exception Handling

Data Structures in Python

• Lists, Tuples, and Sets
• Dictionaries
• String Manipulation
• Comprehensions

Object-Oriented Programming

• Classes and Objects
• Constructors and Destructors
• Inheritance and Polymorphism
• Encapsulation

Advanced Python

• Modules and Packages
• File Handling
• Python Libraries (NumPy, Pandas)
• Regular Expressions
• Introduction to Functional Programming

Basics of DSA

• Algorithm Analysis (Big-O Notation)
• Introduction to Recursion
• Searching Algorithms: Linear and Binary Search
• Sorting Algorithms: Bubble, Selection, Insertion, Merge, Quick

Linear Data Structures

• Arrays and Lists
• Stacks and Queues (Implementation using Lists)
• Applications of Stacks (Expression Evaluation, Parenthesis Matching)
• Applications of Queues (Circular Queue, Priority Queue)

Non-Linear Data Structures

• Trees (Binary Trees, Binary Search Trees)
• Traversal Techniques (Inorder, Preorder, Postorder)
• Graphs (Representation, BFS, DFS)
• Heaps (Min-Heap, Max-Heap)

Advanced DSA Topics

• Hashing (Hash Tables, Hash Maps)
• Dynamic Programming (Fibonacci, Knapsack)
• Divide and Conquer Algorithms
• Backtracking Algorithms (Sudoku Solver, N-Queens Problem)

Introduction to AI

• What is AI?
• Python Libraries for AI (TensorFlow, Keras, Scikit-learn)
• Setting Up AI Development Environment

Machine Learning Basics

• Supervised Learning (Linear Regression, Classification)
• Unsupervised Learning (Clustering, PCA)
• Evaluation Metrics (Accuracy, Precision, Recall)

Natural Language Processing (NLP)

• Text Preprocessing
• Sentiment Analysis
• Building a Chatbot (using NLTK)

AI in Action

• Image Recognition (using TensorFlow/Keras)
• Recommendation System
• Time Series Prediction

Final AI Project

AI Student Form Automation

• Auto-fill forms using OCR and Python
• Predict form fields using machine learning
• Create a web interface for user interaction

Objective: Automate the creation and management of student forms using AI.

Features

1. OCR for Text Recognition

• Extract text from images or scanned documents.
• Python libraries: PyTesseract, OpenCV.

2. Predictive Input Fields

• Predict form entries based on historical data.
• Train a machine learning model using Scikit-learn.

3. Web Interface

• Develop a simple interface using Flask or Django.
• Allow users to upload documents and download the completed form.

4. Database Integration

• Store form data in a database.
• Use SQLite/MySQL.

5. Deployment

• Deploy the solution on the cloud (e.g., AWS, Heroku).

Implementation

• Use OCR to extract student details from images.
• Process data with a predictive ML model for validation and auto-completion.
• Store data in a structured format and provide an easy-to-use web interface.