Machine Learning Guide

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. Links Notes and resources at ocdevel.com/mlg/mla-3 Try a walking desk stay healthy & sharp while you learn & code Data Ingestion and Preprocessing Data Sources and Formats: Datasets commonly originate as CSV (comma-separated values), TSV (tab-separated values), fixed-width files (FWF), JSON from APIs, or directly from databases. Typical applications include structured data (e.g., real estate features) or unstructured data (e.g., natural language corpora for sentiment analysis). Pandas as the Cor