Predictive Analytics & Forecasting

Introduction to Neural Networks

Understanding network architecture and training processes.

Recurrent Neural Networks (RNNs)

Applying RNNs to sequential and time series data.

LSTM and GRU Networks

Advanced architectures for long-term dependencies in sequential data.

Workshop: Neural Network Implementation

Building and training neural networks for forecasting applications.

Association Rule Mining

Discovering relationships between items in large datasets.

Apriori and FP-Growth Algorithms

Efficient methods for identifying frequent itemsets.

Collaborative Filtering Techniques

User-based and item-based approaches for recommendations.

Project: Retail Recommendation Engine

Building a recommendation system based on purchase history data.

Advanced Cross-Validation Techniques

Time-series cross-validation and nested methods.

Performance Metrics for Different Model Types

Selecting appropriate metrics for regression, classification, and time series.

Hyperparameter Optimization

Grid search, random search, and Bayesian optimization approaches.

Lab: Model Comparison Framework

Building a systematic process for model evaluation and selection.

Advanced Feature Creation

Techniques for generating informative features from raw data.

Dimensionality Reduction Methods

PCA, t-SNE, and other techniques for feature space reduction.

Feature Selection Algorithms

Filter, wrapper, and embedded approaches to select optimal features.

Workshop: Feature Engineering Pipeline

Building an automated feature engineering process for predictive models.

Productionizing Predictive Models

Best practices for taking models from development to production.

API Development for Model Access

Creating REST APIs to serve predictions to applications.

Model Monitoring and Maintenance

Techniques for tracking model performance and detecting drift.

Lab: Deploying a Forecasting Solution

End-to-end deployment of a predictive model as a web service.

End-to-End Predictive Solution Development

Comprehensive application of course concepts to a complex business problem.

Communicating Predictions to Stakeholders

Effective presentation of model insights and uncertainty.

Change Management for Analytics Implementation

Strategies for successful adoption of predictive solutions.

Final Project Presentation

Present your predictive solution to industry experts and peers.