Predictive AI Training and Certification in Kathmandu, Nepal

This training program provides a comprehensive introduction to Predictive AI, focusing on how machine learning algorithms and data science techniques can be used to make accurate predictions about future events. Participants will gain an understanding of the fundamentals of predictive modeling, the various types of predictive models, and the tools and technologies used to develop these models. Practical exercises will involve building and testing models using real-world datasets.

Foundations of Predictive AI and Model Building

1. Introduction to Predictive AI
• Overview of predictive AI and its applications in various industries
• Key concepts: supervised learning, regression, and classification
• Practical exercise: Identifying use cases for predictive AI
2. Data Preparation and Preprocessing
• Understanding datasets for predictive modeling
• Techniques for data cleaning, transformation, and feature engineering
• Practical exercise
3. Building Predictive Models
• Introduction to regression and classification algorithms
• Training and testing models with simple datasets
• Practical exercise
4. Model Evaluation Techniques
• Metrics for evaluating predictive models (accuracy, precision, recall, etc.)
• Overfitting and underfitting: Causes and solutions
• Practical exercise

Advanced Techniques and Real-World Applications

5. Advanced Predictive AI Techniques
• Ensemble methods: Random Forest, Gradient Boosting, and XGBoost
• Introduction to time series forecasting with ARIMA and LSTM
• Practical exercise
6. Business Applications of Predictive AI
• Demand forecasting, risk analysis, and customer behavior prediction
• Case studies: Predictive AI in retail, healthcare, and finance
• Practical exercise
7. Deploying Predictive AI Models
• Introduction to deployment using tools like Flask or FastAPI
• Integrating predictive models into applications
• Practical exercise
8. Ethics and Responsible AI
• Ethical considerations in predictive AI: Fairness, bias, and transparency
• Best practices for maintaining ethical AI in business applications
• Discussion: Challenges and future trends in predictive AI