Getting Started with AI Development
Essential guides for starting your AI project with best practices
Project Setup
Environment Setup
Configure your development environment with Python, virtual environments, and essential AI libraries.
- • Python 3.9+ installation
- • Virtual environment setup
- • Package management with pip/conda
- • IDE configuration
Data Preparation
Best practices for collecting, cleaning, and preparing data for AI model training.
- • Data collection strategies
- • Cleaning and validation
- • Feature engineering
- • Train/validation/test splits
Project Structure
my-ai-project/ ├── data/ │ ├── raw/ │ ├── processed/ │ └── external/ ├── models/ │ ├── training/ │ └── inference/ ├── notebooks/ │ └── exploratory/ ├── src/ │ ├── data/ │ ├── features/ │ ├── models/ │ └── visualization/ ├── tests/ ├── requirements.txt └── README.md
First Steps
1
Define Your Problem
Clearly articulate what you want to achieve and how you'll measure success.
2
Gather and Explore Data
Collect relevant data and perform exploratory data analysis to understand patterns.
3
Choose Your Approach
Select appropriate algorithms and frameworks based on your problem type and data.
4
Build and Iterate
Start with a simple baseline model and iteratively improve performance.
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