Course Description
Python Programming with AI: 45-Day Course Syllabus
Course Overview
This intensive 45-day program takes students from Python basics to building practical AI applications. The course combines foundational programming concepts with cutting-edge AI techniques, providing hands on experience with modern AI tools and libraries.
Week 1: Python Foundations
Days 1-3: Python Basics
- Setting up your development environment
- Python syntax and data types
- Variables, operators, and expressions
- Basic input/output operations
- Project: Interactive greeting program
Days 4-7: Control Structures
- Conditional statements (if, elif, else)
- Loops (for and while)
- Functions and parameters
- Basic error handling
- Project: Number guessing game with difficulty levels
Week 2: Data Structures & File Handling
Days 8-10: Collections
- Lists and list comprehensions
- Dictionaries and dictionary comprehensions
- Tuples and sets
- Collection methods and operations
- Project: Contact management system
Days 11-14: File Operations
- Reading and writing text files
- Working with CSV files
- JSON data processing
- Exception handling
- Project: Data extraction and reporting tool
Week 3: Advanced Python & OOP
Days 15-17: Object-Oriented Programming
- Classes and objects
- Inheritance and polymorphism
- Encapsulation and abstraction
- Magic methods
- Project: Library management system
Days 18-21: Advanced Python Concepts
- Lambda functions
- Map, filter, and reduce
- Decorators and generators
- Regular expressions
- Modules and packages
- Project: Text analysis tool with custom modules
Week 4: Python for Data Science
Days 22-24: Data Manipulation
- NumPy arrays and operations
- Pandas DataFrames and Series
- Data cleaning and transformation
- Data visualization with Matplotlib and Seaborn
- Project: Exploratory data analysis dashboard
Days 25-28: Introduction to AI & ML
- AI concepts and terminology
- Types of machine learning
- Data preprocessing for ML
- Feature engineering
- Training and testing models
- Project: Predictive model using Scikit-learn
Week 5: Applied Machine Learning
Days 29-31: Supervised Learning
- Linear and logistic regression
- Decision trees and random forests
- Support vector machines
- Model evaluation metrics
- Project: Customer churn prediction system
Days 32-35: Introduction to Deep Learning
- Neural network fundamentals
- TensorFlow and Keras basics
- Building your first neural network
- Training and optimization
- Project: Image classification application
- Week 6: Advanced AI Applications
Days 36-38: Natural Language Processing
- Text preprocessing techniques
- Sentiment analysis
- Named entity recognition
- Introduction to transformers
- Project: AI-powered text summarization tool
Days 39-42: AI-Assisted Development
- Using GitHub Copilot for coding
- Prompt engineering for code generation
- AI-assisted debugging
- Best practices for AI collaboration
- Project: Building a recommendation system with AI assistance
Days 43-45: Capstone Project & Course Conclusion
- Final project planning and implementation
- Code optimization and best practices
- Deployment considerations
- Project presentations
- Course review and career paths
Daily Structure
- 1-hour theoretical session
- 2-hour hands-on coding practice
- 30-minute Q&A and problem-solving
- Daily coding challenges
Learning Resources
- Custom course textbook
- Interactive Jupyter notebooks
- Curated video tutorials
- AI assistant for programming help
- Cloud-based development environment
Assessment Method
- Daily coding exercises (20%)
- Weekly projects (40%)
- Capstone project (30%)
- Participation and peer reviews (10%)
Course Highlights
- Learn to leverage AI tools to accelerate programming
- Build a portfolio of 10+ Python projects
- Receive personalized feedback on your code
- Join a community of AI-focused developers
- Earn a verifiable digital certificate
Post-Course Benefits
- 3 months access to course materials
- Private alumni community membership
- Monthly coding challenges
- Priority access to advanced courses
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