🤖 AI Internship Program

📅 Day 1-2: Python Fundamentals I

• Python installation and environment setup (Anaconda, VS Code)
• Variables, data types, operators
• Control structures (if-else, loops)
• Functions and modules
• File handling

Hands-on: Simple calculator, file processing utility, student grade management system

Assignment: Build a contact management CLI application

📅 Day 3-4: Python Fundamentals II

• Object-Oriented Programming (OOP)
• Classes, objects, inheritance
• Exception handling
• Python standard library
• Working with packages (pip)

Hands-on: Library management system (OOP), error handling, creating custom modules

Assignment: Build a bank account management system using OOP

📅 Day 5: Data Structures & Algorithms

• Lists, tuples, sets, dictionaries
• List comprehensions
• Lambda functions, map, filter, reduce
• Basic algorithms (sorting, searching)
• Time complexity basics

Hands-on: Data processing with collections, algorithm implementations

Assignment: Implement search and sort algorithms

📅 Day 6-7: Data Science Libraries

• NumPy: Arrays, operations, linear algebra
• Pandas: DataFrames, data manipulation
• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Matplotlib & Seaborn for visualization

Hands-on: Load and analyze real datasets, data cleaning pipeline, create visualizations

Assignment: Complete EDA on provided dataset

📅 Day 8-9: Machine Learning Fundamentals

• Introduction to Machine Learning
• Supervised vs Unsupervised learning
• Train-test split, cross-validation
• Scikit-learn library
• Linear & Logistic Regression
• Decision Trees and Random Forests
• Model evaluation metrics

Hands-on: House price prediction, email spam classification, customer segmentation

Assignment: Build and evaluate ML model on real dataset

📅 Day 10: ML Project Day

• End-to-end ML project workflow
• Feature engineering
• Hyperparameter tuning
• Model deployment basics
• Creating API with Flask/FastAPI

Mini Project: Deploy ML model as web service

📅 Day 11-12: Deep Learning Basics

• Introduction to Neural Networks
• TensorFlow and Keras fundamentals
• Building and training neural networks
• Convolutional Neural Networks (CNNs)
• Image classification and computer vision
• Transfer learning techniques

Hands-on: Build image classifiers using CNNs

Assignment: Build image classifier for custom dataset

📅 Day 13-14: Large Language Models (LLMs)

• Introduction to LLMs and Transformers
• Working with pre-trained models (GPT, BERT)
• Prompt engineering techniques
• Fine-tuning LLMs
• LangChain and AI application development
• Building chatbots and conversational AI

Hands-on: Build applications using LLMs

Assignment: Build Q&A chatbot using LLMs

📅 Day 15: NLP & Advanced Topics

• Natural Language Processing fundamentals
• Text preprocessing and tokenization
• Sentiment analysis
• Named Entity Recognition (NER)
• Text generation and summarization

Mini Project: Build text analysis application

📅 Day 16-17: Software Engineering Practices

• Git and version control
• Code quality and best practices
• Unit testing and test-driven development
• Debugging techniques
• Documentation and code reviews
• Collaborative development workflows

Hands-on: Implement testing and version control

Assignment: Refactor previous project with tests

📅 Day 18: DevOps Fundamentals

• Introduction to DevOps practices
• Docker and containerization
• CI/CD pipelines
• Cloud platforms (AWS/Azure/GCP basics)
• Application deployment strategies

Hands-on: Containerize and deploy applications

Assignment: Deploy ML application with Docker

📅 Day 19: MLOps Introduction

• MLOps principles and practices
• Model versioning and registry
• Experiment tracking (MLflow, Weights & Biases)
• Model monitoring and maintenance
• Production ML pipelines

Hands-on: Implement MLOps workflows

Assignment: Implement MLOps for previous ML project

📅 Day 20: Capstone Project Day

• End-to-end AI project development
• Integration of all learned concepts
• Project presentation and demonstration
• Code review and best practices
• Deployment to production environment

Deliverable: Complete end-to-end project with deployment