Data Preparation for Machine Learning
Contact us to book this course
Machine Learning and AI
On-Site, Virtual
1 day
This course focuses on the fundamentals of preparing data for machine learning using Databricks. Participants will learn essential skills for exploring, cleaning, and organizing data tailored for traditional machine learning applications. Key topics include data visualization, feature engineering, and optimal feature storage strategies. Through practical exercises, participants will gain hands-on experience in efficiently preparing data sets for machine learning within the Databricks. This course is designed for associate-level data scientists and machine learning practitioners. and individuals seeking to enhance their proficiency in data preparation, ensuring a solid foundation for successful machine learning model deployment.
Objectives
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Describe the Databricks Data Intelligence Platform and its features for machine learning.
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Explain the data storage and governance features of Databricks.
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Perform exploratory data analysis and feature engineering using Spark and integrated visualisation tools.
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Perform data pre-processing for missing data handling, data encoding, and data standardisation.
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Utilise the Feature Store for storing and retrieving features.
Prerequisites
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Familiarity with Databricks workspace and notebooks
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Familiarity with Delta Lake and Lakehouse
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Intermediate-level knowledge of Python
Course outline
- Fundamentals of Data Preparation and Feature Engineering
- Data Imputation
- Data Encoding
- Data Standardization
- Feature Engineering Pipelines
- Introduction to Feature Store
- Feature Engineering with Feature Store
