This module guides learners through the crucial first steps of any ML project: defining clear problem statements and collecting quality data. You'll learn to formulate well-scoped ML problems based on real-world use cases, identify business and technical constraints that influence model selection, and develop skills in sourcing, collecting, and cleaning data to ensure relevance, consistency, and usability.

In this module, you'll learn to analyze data distributions, detect patterns, and identify anomalies through statistical and visual methods. Through hands-on practice, you'll apply feature selection and engineering techniques to enhance model performance, and learn to handle data imbalances using techniques such as oversampling, undersampling, and SMOTE.

This module focuses on selecting appropriate models based on data characteristics and project requirements. You'll implement multiple models, comparing classical ML, deep learning, and generative AI approaches. Through practical exercises, you'll learn to select and implement models that best fit your use case, and use ensemble techniques to improve model performance.

In this module, you'll learn to evaluate models using appropriate metrics for different types of ML tasks. You'll master model interpretation using feature importance methods and address fairness and bias considerations. The module emphasizes practical approaches to ensuring model reliability and ethical implementation.

The final module covers the practical aspects of deploying and maintaining ML models. You'll understand different deployment strategies and learn how to monitor models for performance drift and decay. While focusing on conceptual understanding rather than deep technical implementation, you'll learn when and how models should be retrained and maintained in production environments.