ml.qmd

---
title: "Machine Learning"
subtitle: "AI Development"
---

# Machine Learning & AI Development

## Current AI Work

### AI Agent Development:
- **Real-time Processing**: AI agents that process industrial sensor data in real-time
- **Predictive Modeling**: Models to predict equipment failures and maintenance needs
- **Anomaly Detection**: Systems to identify unusual patterns in industrial data
- **Automated Decisions**: AI systems that make operational decisions autonomously

### Technical Implementation:
- **Python ML Stack**: scikit-learn, TensorFlow, PyTorch for model development
- **Real-time Inference**: Models that process data streams with minimal latency
- **Model Monitoring**: Systems to track model performance and drift
- **Integration**: Connecting AI models with existing industrial systems

## Planned Portfolio Projects

### Project 1: Predictive Modeling with Historical Time-Series Data
- **Objective**: ML model (LSTM/XGBoost) that analyzes historical data to forecast future trends
- **Application**: Industrial output, resource demand, or financial metrics forecasting
- **Features**: Dynamic visualizations and real-time predictions for operational decision-making
- **Techniques**: LSTM, XGBoost, time-series analysis
- **Tools**: Python, LSTM, XGBoost

### Project 2: AI-Powered Script-to-Video Generation Pipeline
- **Objective**: Comprehensive pipeline that transforms written scripts into short video content
- **Technology**: LLMs, image/video APIs, and voice synthesis
- **Features**: Advanced AI coordination across language processing, media generation, and rendering
- **Techniques**: LLM integration, video generation, voice synthesis
- **Tools**: Python, LLM, Video Generation APIs

### Project 3: Natural Language Agent for Structured Data Analysis
- **Objective**: Intelligent AI agent that interprets natural language queries and analyzes data sources
- **Application**: Uploaded or connected data sources (e.g., CSVs)
- **Features**: Leverages LLM technology to provide accurate, human-readable answers and visual insights
- **Techniques**: LLM integration, natural language processing, data analysis
- **Tools**: Python, LLM, Data Analysis libraries

## Skills:
- **Supervised Learning**: Classification, regression, time-series forecasting
- **Unsupervised Learning**: Clustering, anomaly detection
- **Deep Learning**: Neural networks, CNNs, RNNs, LSTM
- **Model Deployment**: Docker containers, API development
- **Data Engineering**: Feature engineering, data preprocessing

*These projects will demonstrate both theoretical understanding and practical application of machine learning.*