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Practical workflow of loading, cleaning, and storing large datasets for machine learning, moving from ingesting raw CSVs or JSON files with pandas to saving processed datasets and neural network weights using HDF5 for efficient numerical storage. It clearly distinguishes among storage options—explaining when to use HDF5, pickle files, or SQL databases—while highlighting how libraries like pandas, TensorFlow, and Keras interact with these formats and why these choices matter for production pipelines.
LinksData Sources and Formats:
Pandas as the Core Ingestion Tool:
Data Encoding for Machine Learning:
HDF5 for Storing Processed Arrays:
Pickle for Python Objects:
SQL Databases and Spreadsheets:
Typical Process:
Best Practices and Progression:
4.9
759759 ratings
Practical workflow of loading, cleaning, and storing large datasets for machine learning, moving from ingesting raw CSVs or JSON files with pandas to saving processed datasets and neural network weights using HDF5 for efficient numerical storage. It clearly distinguishes among storage options—explaining when to use HDF5, pickle files, or SQL databases—while highlighting how libraries like pandas, TensorFlow, and Keras interact with these formats and why these choices matter for production pipelines.
LinksData Sources and Formats:
Pandas as the Core Ingestion Tool:
Data Encoding for Machine Learning:
HDF5 for Storing Processed Arrays:
Pickle for Python Objects:
SQL Databases and Spreadsheets:
Typical Process:
Best Practices and Progression:
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