Data Preparation for Machine Learning
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.
Languages Available: English | 日本語 | Português BR | 한국어
In this course, the content was developed for participants with these skills/knowledge/abilities:
• Completed the Get Started with Databricks for Machine Learning (Onboarding) course or possess equivalent foundational knowledge of working in the Databricks environment.
- Learners should be familiar with navigating the Databricks workspace, creating and running notebooks, and understanding the basic machine learning workflow on Databricks. This course builds on that foundation to focus on data preparation for machine learning.
• Intermediate-level proficiency in Python programming for data preparation and analysis.
- Learners should be comfortable using libraries such as pandas, numpy, and scikit-learn for data manipulation, handling missing values, and basic feature transformations.
• Basic understanding of machine learning fundamentals.
- This includes familiarity with concepts such as training and test datasets, feature engineering, and model development pipelines.
• Familiarity with Databricks platform workflows.
- Learners should be able to perform basic tasks such as creating clusters, running code in notebooks, and using common notebook operations.
• Basic knowledge of data formats and lakehouse concepts.
- Learners should be familiar with common data formats such as CSV, JSON, and Parquet, and have introductory knowledge of Delta Lake and the Lakehouse architecture.
• Foundational understanding of exploratory data analysis and basic statistics.
- This includes awareness of data distributions, missing values, outliers, and simple data visualization techniques used to assess data quality.
Outline
Managing and Exploring Data
• Managing and Exploring Data in the Lakehouse
• Demo: Load and Explore Data
• Lab: Load and Explore Data
Data Preparation and Feature Engineering
• Fundamentals of Data Preparation and Feature Engineering
• Data Imputation
• Data Encoding
• Data Standardization
• Demo: Data Imputation and Transformation Pipeline
• Demo: Build a Feature Engineering Pipeline
• Lab: Build a Feature Engineering Pipeline
Feature Store
• Demo: Using Feature Store for Feature Engineering
• Lab: Feature Engineering with Feature Store
Upcoming Public Classes
Date | Time | Your Local Time | Language | Price |
|---|---|---|---|---|
Jun 02 | 08 AM - 12 PM (Asia/Kolkata) | - | English | $750.00 |
Jul 02 | 01 PM - 05 PM (Australia/Sydney) | - | English | $750.00 |
Jul 02 | 09 AM - 01 PM (America/New_York) | - | English | $750.00 |
Public Class Registration
If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.
Private Class Request
If your company is interested in private training, please submit a request.
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