This module provides comprehensive training in implementing machine learning solutions using the PySpark ML library for big data environments. Students will master ML pipelines, transformers, and estimators while learning to develop scalable regression, classification, and clustering models. The module emphasizes practical implementation skills and platform selection strategies for enterprise ML deployments across Azure Databricks, Microsoft Fabric, and HDInsight.

This module focuses on processing and analyzing large volumes of unstructured text data using distributed computing frameworks. Students will learn to apply NLP techniques using scalable architectures, implement text classification and sentiment analysis systems, and extract entities and relationships from massive text corpora. The module emphasizes practical skills for handling enterprise-scale text analytics requirements while integrating with Azure Cognitive Services for enhanced capabilities.

This module introduces deep learning fundamentals and advanced architectures specifically adapted for big data environments. Students will learn to implement neural networks for big data applications, apply transfer learning techniques with pre-trained models, and scale deep learning training across distributed clusters using modern frameworks and optimization techniques.