Data Ready: Prepare, Clean, and Structure Data for Machine Learning Success

In a world where over 50 zettabytes of data are generated annually-and growing-knowing how to turn raw data into machine-ready input is a must-have skill. Before any model can make predictions or drive decisions, data must be carefully extracted, cleaned, transformed, and structured to meet the demands of modern algorithms.

This foundational course in the Applied Machine Learning Certificate equips you with the essential techniques and tools to prepare real-world data for machine learning. You'll gain hands-on experience working with open-source tools to extract data from multiple sources, resolve inconsistencies, address bias, and ensure data is properly formatted for effective algorithm performance.

Whether you're an aspiring data scientist, analyst, or technical professional looking to advance into AI and machine learning, this course provides the critical first step. With practical, project-based learning and access to freely available tools and datasets, you'll be able to immediately apply what you learn to future modeling work.

All tools and data used in this course are open source and available for continued use before, during, and after the course.

What You Will Learn:

• Establish a work environment conducive to data science

• Extract data from databases, APIs, and web scraping

• Preprocess and clean data

• Address data fairness

• Normalize data

• Retrieve information from data

• Format data for machine learning

Earn 1.4 Continuing Education Units (CEUs) and build a strong foundation in the most important-and often most overlooked-step of machine learning: preparing the data.