Create Python AI Engineering course repository structure with 12 pillars

README.md

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-# https-github.com-python-ai-engineering-course-code
+# Python AI Engineering Course
+
+Welcome to the Python AI Engineering Course! This comprehensive course is designed to equip you with the skills and knowledge needed to excel in AI engineering using Python.
+
+## Course Overview
+
+This course covers the fundamental and advanced concepts of Python programming with a focus on AI engineering applications. Through hands-on examples, labs, and projects, you'll learn to build robust AI solutions using industry-standard tools and best practices.
+
+## Course Structure
+
+The course is organized into 12 pillars, each focusing on a specific aspect of Python and AI engineering:
+
+- **pillar-01**: Python Fundamentals
+- **pillar-02**: Data Structures and Algorithms
+- **pillar-03**: Object-Oriented Programming
+- **pillar-04**: Functional Programming
+- **pillar-05**: Data Processing with NumPy and Pandas
+- **pillar-06**: Testing with Pytest
+- **pillar-07**: Type Checking with MyPy
+- **pillar-08**: Data Validation with Pydantic
+- **pillar-09**: API Development with FastAPI
+- **pillar-10**: Machine Learning Basics
+- **pillar-11**: Deep Learning Fundamentals
+- **pillar-12**: AI Engineering Best Practices
+
+Each pillar contains:
+- **examples/**: Practical code examples demonstrating key concepts
+- **labs/**: Hands-on exercises to practice what you've learned
+- **project/**: A comprehensive project to apply your knowledge
+- **solutions/**: Solutions to labs and exercises
+- **README.md**: Detailed overview of the pillar's content
+
+## Getting Started
+
+### Prerequisites
+
+- Python 3.8 or higher
+- Basic understanding of programming concepts
+
+### Installation
+
+1. Clone this repository:
+   ```bash
+   git clone https://github.com/piatrips/https-github.com-python-ai-engineering-course-code.git
+   cd https-github.com-python-ai-engineering-course-code
+   ```
+
+2. Install the required dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+## Dependencies
+
+This course uses the following Python packages:
+- **numpy**: Numerical computing library
+- **pandas**: Data manipulation and analysis
+- **pytest**: Testing framework
+- **mypy**: Static type checker
+- **pydantic**: Data validation using Python type annotations
+- **fastapi**: Modern web framework for building APIs
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+## Contact
+
+For questions or feedback, please open an issue in this repository.

pillar-01/README.md

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+# Pillar 01: Python Fundamentals
+
+## Overview
+
+This pillar covers the foundational concepts of Python programming, including:
+- Python syntax and semantics
+- Variables and data types
+- Control flow (if/else, loops)
+- Functions and modules
+- Basic input/output operations
+
+## Contents
+
+- **examples/**: Code examples demonstrating Python fundamentals
+- **labs/**: Hands-on exercises to practice basic Python skills
+- **project/**: A comprehensive project applying fundamental concepts
+- **solutions/**: Solutions to lab exercises
+
+## Learning Objectives
+
+By the end of this pillar, you will be able to:
+- Write and execute basic Python programs
+- Understand and use Python's built-in data types
+- Implement control flow logic
+- Create and use functions
+- Work with Python modules
+
+## Getting Started
+
+Navigate to the `examples/` directory to see practical demonstrations of the concepts covered in this pillar.

pillar-02/README.md

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+# Pillar 02: Data Structures and Algorithms
+
+## Overview
+
+This pillar explores Python's data structures and fundamental algorithms:
+- Lists, tuples, sets, and dictionaries
+- List comprehensions
+- Common algorithms (sorting, searching)
+- Time and space complexity
+- Algorithm optimization
+
+## Contents
+
+- **examples/**: Code examples demonstrating data structures and algorithms
+- **labs/**: Hands-on exercises to practice implementing algorithms
+- **project/**: A comprehensive project applying data structures
+- **solutions/**: Solutions to lab exercises
+
+## Learning Objectives
+
+By the end of this pillar, you will be able to:
+- Effectively use Python's built-in data structures
+- Implement common algorithms
+- Analyze algorithm complexity
+- Choose appropriate data structures for different problems
+- Optimize code performance
+
+## Getting Started
+
+Navigate to the `examples/` directory to see practical demonstrations of the concepts covered in this pillar.

pillar-03/README.md

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+# Pillar 03: Object-Oriented Programming
+
+## Overview
+
+This pillar covers object-oriented programming (OOP) concepts in Python:
+- Classes and objects
+- Inheritance and polymorphism
+- Encapsulation and abstraction
+- Magic methods and operator overloading
+- Design patterns
+
+## Contents
+
+- **examples/**: Code examples demonstrating OOP concepts
+- **labs/**: Hands-on exercises to practice OOP principles
+- **project/**: A comprehensive project using OOP design
+- **solutions/**: Solutions to lab exercises
+
+## Learning Objectives
+
+By the end of this pillar, you will be able to:
+- Design and implement classes and objects
+- Apply inheritance and polymorphism
+- Use encapsulation to protect data
+- Implement common design patterns
+- Write maintainable object-oriented code
+
+## Getting Started
+
+Navigate to the `examples/` directory to see practical demonstrations of the concepts covered in this pillar.

pillar-04/README.md

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+# Pillar 04: Functional Programming
+
+## Overview
+
+This pillar introduces functional programming concepts in Python:
+- Lambda functions
+- Map, filter, and reduce
+- Decorators
+- Generators and iterators
+- Functional programming paradigms
+
+## Contents
+
+- **examples/**: Code examples demonstrating functional programming
+- **labs/**: Hands-on exercises to practice functional concepts
+- **project/**: A comprehensive project using functional programming
+- **solutions/**: Solutions to lab exercises
+
+## Learning Objectives
+
+By the end of this pillar, you will be able to:
+- Write and use lambda functions
+- Apply higher-order functions
+- Create and use decorators
+- Implement generators and iterators
+- Combine functional and object-oriented approaches
+
+## Getting Started
+
+Navigate to the `examples/` directory to see practical demonstrations of the concepts covered in this pillar.

pillar-05/README.md

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+# Pillar 05: Data Processing with NumPy and Pandas
+
+## Overview
+
+This pillar covers data processing using NumPy and Pandas:
+- NumPy arrays and operations
+- Pandas DataFrames and Series
+- Data cleaning and transformation
+- Data analysis and aggregation
+- Working with different data formats
+
+## Contents
+
+- **examples/**: Code examples demonstrating NumPy and Pandas usage
+- **labs/**: Hands-on exercises for data processing tasks
+- **project/**: A comprehensive data analysis project
+- **solutions/**: Solutions to lab exercises
+
+## Learning Objectives
+
+By the end of this pillar, you will be able to:
+- Perform numerical computations with NumPy
+- Manipulate and analyze data with Pandas
+- Clean and transform datasets
+- Perform data aggregation and grouping
+- Import and export data in various formats
+
+## Getting Started
+
+Navigate to the `examples/` directory to see practical demonstrations of the concepts covered in this pillar.

pillar-06/README.md

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+# Pillar 06: Testing with Pytest
+
+## Overview
+
+This pillar focuses on testing Python applications using Pytest:
+- Writing unit tests
+- Test fixtures and parametrization
+- Mocking and patching
+- Code coverage
+- Test-driven development (TDD)
+
+## Contents
+
+- **examples/**: Code examples demonstrating testing practices
+- **labs/**: Hands-on exercises for writing tests
+- **project/**: A comprehensive project with full test coverage
+- **solutions/**: Solutions to lab exercises
+
+## Learning Objectives
+
+By the end of this pillar, you will be able to:
+- Write effective unit tests with Pytest
+- Use fixtures and parametrization
+- Mock external dependencies
+- Measure and improve code coverage
+- Apply test-driven development practices
+
+## Getting Started
+
+Navigate to the `examples/` directory to see practical demonstrations of the concepts covered in this pillar.

pillar-07/README.md

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+# Pillar 07: Type Checking with MyPy
+
+## Overview
+
+This pillar covers static type checking in Python using MyPy:
+- Type annotations and hints
+- Generic types and type variables
+- Protocol classes
+- Type checking configuration
+- Gradual typing strategies
+
+## Contents
+
+- **examples/**: Code examples demonstrating type annotations
+- **labs/**: Hands-on exercises for adding type hints
+- **project/**: A comprehensive typed Python project
+- **solutions/**: Solutions to lab exercises
+
+## Learning Objectives
+
+By the end of this pillar, you will be able to:
+- Add type annotations to Python code
+- Use MyPy for static type checking
+- Work with generic types and protocols
+- Configure MyPy for different projects
+- Apply gradual typing to existing codebases
+
+## Getting Started
+
+Navigate to the `examples/` directory to see practical demonstrations of the concepts covered in this pillar.

pillar-08/README.md

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+# Pillar 08: Data Validation with Pydantic
+
+## Overview
+
+This pillar explores data validation using Pydantic:
+- Pydantic models and fields
+- Data validation and parsing
+- Custom validators
+- Settings management
+- JSON schema generation
+
+## Contents
+
+- **examples/**: Code examples demonstrating Pydantic usage
+- **labs/**: Hands-on exercises for data validation
+- **project/**: A comprehensive project using Pydantic
+- **solutions/**: Solutions to lab exercises
+
+## Learning Objectives
+
+By the end of this pillar, you will be able to:
+- Create Pydantic models for data validation
+- Implement custom validators
+- Parse and validate complex data structures
+- Manage application settings with Pydantic
+- Generate JSON schemas from models
+
+## Getting Started
+
+Navigate to the `examples/` directory to see practical demonstrations of the concepts covered in this pillar.

pillar-09/README.md

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+# Pillar 09: API Development with FastAPI
+
+## Overview
+
+This pillar covers building modern APIs using FastAPI:
+- FastAPI basics and routing
+- Request and response models
+- Dependency injection
+- Authentication and authorization
+- API documentation with OpenAPI
+
+## Contents
+
+- **examples/**: Code examples demonstrating FastAPI features
+- **labs/**: Hands-on exercises for building APIs
+- **project/**: A comprehensive API project
+- **solutions/**: Solutions to lab exercises
+
+## Learning Objectives
+
+By the end of this pillar, you will be able to:
+- Build RESTful APIs with FastAPI
+- Implement request validation and response models
+- Use dependency injection
+- Add authentication and authorization
+- Generate automatic API documentation
+
+## Getting Started
+
+Navigate to the `examples/` directory to see practical demonstrations of the concepts covered in this pillar.

pillar-10/README.md

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+# Pillar 10: Machine Learning Basics
+
+## Overview
+
+This pillar introduces fundamental machine learning concepts:
+- Supervised and unsupervised learning
+- Feature engineering
+- Model training and evaluation
+- Cross-validation techniques
+- Common ML algorithms
+
+## Contents
+
+- **examples/**: Code examples demonstrating ML concepts
+- **labs/**: Hands-on exercises for building ML models
+- **project/**: A comprehensive machine learning project
+- **solutions/**: Solutions to lab exercises
+
+## Learning Objectives
+
+By the end of this pillar, you will be able to:
+- Understand fundamental ML concepts
+- Prepare data for machine learning
+- Train and evaluate ML models
+- Apply cross-validation techniques
+- Choose appropriate algorithms for different problems
+
+## Getting Started
+
+Navigate to the `examples/` directory to see practical demonstrations of the concepts covered in this pillar.