Common Data Formats in Machine Learning

📄️ CSV

Understanding the Comma-Separated Values format: its role in ML, performance trade-offs, and best practices for ingestion.

Handling .xlsx and .xls files in ML pipelines: managing multi-sheet workbooks, data types, and conversion pitfalls.

Mastering JSON for Machine Learning: handling nested data, converting dictionaries, and efficient parsing for NLP pipelines.

Understanding Columnar storage, compression benefits, and why Parquet is the preferred format for high-performance ML pipelines.

Handling hierarchical data in XML: parsing techniques, its role in Computer Vision annotations, and converting XML to ML-ready formats.