Machine Learning Practitioner

DATB-DPML : Data Preparation for Machine Learning

Course Duration

4 Hours

Delivery

ILT/VILT

Overview

Overview Course Curriculum Training Schedule Exam & Certification FAQs

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.

What You'll Learn

Use the Databricks Data Intelligence Platform for machine learning-oriented data preparation.
Store, manage, and reuse features using the Databricks Feature Store for scalable ML solutions.
Apply optimal feature storage strategies and data visualization techniques tailored to ML workflows.

Who Should Attend

Data scientists, ML engineers and analytics practitioners preparing datasets for machine-learning workflows on the Databricks Lakehouse Platform.
Professionals responsible for cleaning, transforming, enriching and engineering features from raw, semi-structured or structured datasets before model training.
Individuals using Python, Spark or Delta Lake who want to strengthen their skills in data wrangling, feature extraction, handling missing values and preparing high-quality training datasets.
Practitioners working with MLflow, Feature Store or AutoML who need to streamline data-preparation steps for reproducible and scalable ML pipelines.
Teams building end-to-end machine-learning solutions and aiming to standardise data-preparation best practices for improved model performance and reliability.

Prerequisites

Familiarity with Databricks workspace and notebooks
Familiarity with Delta Lake and Lakehouse
Intermediate-level knowledge of Python

Learning Journey

Coming Soon...

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

Frequently Asked Questions (FAQs)

None

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