TRAINING COURSE: Making Decisions Using Artificial Intelligence for Files and Their Preparation

Data Collection and File Handling

        Collecting Data for AI Applications

•         Methods for data collection (web scraping, APIs, sensors, etc.)
•         Ethical considerations in data collection
•        Ensuring data privacy and security
•         Data licensing and compliance issues

•    Reading and writing files in different formats
•    File handling libraries and tools (Pandas, Openpyxl, JSON libraries)
•    Organizing and storing files efficiently
•    Batch processing of files
•    File versioning and management systems
•    Automating file handling processes

        Preprocessing Files for AI

•    Cleaning and normalizing data within files
•    Handling missing data in files
•    Detecting and correcting errors in files
•    Data transformation techniques (scaling, encoding, etc.)
•    Tools for data preprocessing

Feature Engineering and Selection from Files

        Understanding Features and Their Importance

•    Definition and types of features in datasets
•    Role of feature engineering in improving model performance
•    Feature extraction from different file types (text, CSV, etc.)

       Techniques for Feature Engineering

•    Creating new features from existing data
•    Handling categorical and numerical features
•    Temporal feature extraction from time-series files
•   Feature scaling and normalization

•    Importance of selecting relevant features
•    Filter, wrapper, and embedded methods for feature selection
•    Dimensionality reduction techniques
• Tools and libraries for feature engineering and selection

Applying AI in Decision Making within an Organization

       Integrating AI into Organizational Decision Making

•    Identifying key decision-making areas that can benefit from AI
•    Mapping out processes and data flows within the organization
•    Aligning AI applications with organizational goals and strategies
•   Ensuring stakeholder buy-in and collaboration across departments

        Using AI to Analyze Organizational Data

•    Collecting and consolidating data from various organizational files
•    Applying AI models to detect patterns and insights in organizational data
•    Automating routine decision-making tasks with AI-driven insights
•   Tools and platforms for implementing AI in organizational contexts

        Developing AI-Powered Decision Support Systems

•    Designing AI systems to support decision-making processes
•    Integrating AI systems with existing organizational software and databases
•    Real-time data processing and decision making
•    User interfaces and dashboards for AI-driven decision support

Deployment and Maintenance of AI Models Using Files

        Deploying AI Models

•    Methods for deploying models (cloud, on-premise, edge)
•    Preparing files for deployment (formatting, compression, etc.)
•    Tools for deploying AI models (Flask, Docker, Kubernetes)

        Monitoring and Maintaining Deployed Models

•    Setting up monitoring for model performance
•    Automating retraining with new data files
•    Handling model drift and data drift
•    Version control for models and data files