In this course, you’ll learn the fundamentals of preparing data for machine learning using Databricks. We’ll cover topics like exploring, cleaning, and organizing data tailored for traditional machine learning applications. We’ll also cover data visualization, feature engineering, and optimal feature storage strategies. By building a strong foundation in data preparation, this course equips you with the essential skills to create high-quality datasets that can power accurate and reliable machine learning and AI models. Whether you're developing predictive models or enabling downstream AI applications, these capabilities are critical for delivering impactful, data-driven solutions. Pre-requisites: Familiarity with Databricks workspace, notebooks, as well as Unity Catalog. An intermediate level knowledge of Python (scikit-learn, Matplotlib), Pandas, and PySpark. As well as with concepts of exploratory data analysis, feature engineering, standardization, and imputation methods). Labs: Yes Certification Path: Databricks Certified Machine Learning Associate