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Feature Engineering

Google Cloud

This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

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What's inside

Syllabus

Introduction

Introduction to Vertex AI Feature Store

Raw Data to Features

Feature Engineering

Preprocessing and Feature Creation

Feature Crosses - TensorFlow Playground

Introduction to TensorFlow Transform

Summary

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops an understanding of the benefits of using Vertex AI Feature Store

Provides examples of how to improve the accuracy of ML models

Explores techniques for identifying highly relevant data

Provides hands-on labs for practical implementation

Features a range of content for various learning styles

Requires prior knowledge in BigQuery ML, Keras, and TensorFlow

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Career center

Learners who complete Feature Engineering will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course may be helpful for Data Analysts by providing them with the skills to prepare raw data for analysis and extract meaningful insights through feature engineering. The course covers topics such as data preprocessing, feature selection, and feature transformation, which are essential for building robust and interpretable data analysis models.

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Data Scientist

Data Scientists leverage large datasets to create valuable business insights through modeling, statistical analysis, and machine learning. This course may be helpful for aspiring Data Scientists by providing them with the skills to identify and extract useful features from raw data using Vertex AI Feature Store, BigQuery ML, TensorFlow, and other tools. The focus on feature engineering and model accuracy enhancement makes this course a valuable resource for individuals looking to build a strong foundation in data science.

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Machine Learning Engineer

Machine Learning Engineers design and develop machine learning models to solve complex business problems. This course may be helpful for Machine Learning Engineers by providing them with the knowledge and hands-on experience to improve the accuracy of ML models through effective feature engineering techniques. The course covers topics such as feature preprocessing, feature creation, feature crosses, and TensorFlow Transform, which are essential for building high-performing ML models.

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Business Analyst

Business Analysts analyze business processes and data to identify areas for improvement. This course may be helpful for Business Analysts by providing them with the skills to extract meaningful insights from data through feature engineering. The course covers topics such as data preprocessing, feature selection, and feature transformation, which are essential for building data-driven business analysis models.

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Software Engineer

Software Engineers design, develop, and maintain software applications. This course may be helpful for Software Engineers by providing them with the knowledge and skills to incorporate feature engineering techniques into their software development process. The course covers topics such as data preprocessing, feature selection, and feature transformation, which are essential for building high-quality software applications that can handle complex data.

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Researcher

Researchers conduct scientific investigations to advance knowledge and understanding. This course may be helpful for Researchers by providing them with the skills to extract meaningful features from data and build models for hypothesis testing and theory development. The course covers topics such as feature engineering, data analysis, and scientific modeling, which are essential for building robust and reproducible research findings.

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Statistician

Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course may be helpful for Statisticians by providing them with the skills to extract meaningful features from data and build statistical models for prediction and inference. The course covers topics such as feature engineering, statistical modeling, and data visualization, which are essential for building robust and interpretable statistical models.

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Product Manager

Product Managers define and manage the development of new products and features. This course may be helpful for Product Managers by providing them with the knowledge and skills to identify and prioritize features that meet customer needs and drive business value. The course covers topics such as feature engineering, feature prioritization, and data-driven decision-making, which are essential for building successful products.

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Data Engineer

Data Engineers design and build data pipelines and infrastructure. This course may be helpful for Data Engineers by providing them with the knowledge and skills to prepare and transform data for feature engineering. The course covers topics such as data preprocessing, data cleaning, and data integration, which are essential for building robust and scalable data pipelines.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course may be helpful for Quantitative Analysts by providing them with the skills to extract meaningful features from financial data and build predictive models for risk assessment and portfolio optimization. The course covers topics such as feature engineering, financial modeling, and risk management, which are essential for building robust and profitable quantitative investment strategies.

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Consultant

Consultants provide expert advice and guidance to businesses on a variety of topics. This course may be helpful for Consultants by providing them with the skills to extract meaningful features from data and build models for problem-solving and decision-making. The course covers topics such as feature engineering, data analysis, and business intelligence, which are essential for building robust and actionable consulting recommendations.

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Operations Manager

Operations Managers plan and execute operations to achieve business goals. This course may be helpful for Operations Managers by providing them with the skills to identify and prioritize operational improvements based on data. The course covers topics such as feature engineering, process improvement, and data-driven decision-making, which are essential for building efficient and effective operations.

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Sales Manager

Sales Managers lead and motivate sales teams to achieve revenue targets. This course may be helpful for Sales Managers by providing them with the skills to identify and prioritize customer needs based on their features. The course covers topics such as feature engineering, customer relationship management, and sales forecasting, which are essential for building successful sales teams.

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Project Manager

Project Managers plan, execute, and close projects. This course may be helpful for Project Managers by providing them with the skills to identify and prioritize features for project success. The course covers topics such as feature engineering, project management, and stakeholder management, which are essential for building successful projects.

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Marketing Manager

Marketing Managers plan and execute marketing campaigns to promote products and services. This course may be helpful for Marketing Managers by providing them with the skills to identify and target customer segments based on their features. The course covers topics such as feature engineering, market segmentation, and campaign management, which are essential for building successful marketing campaigns.

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Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Feature Engineering.

Feature Engineering for Machine Learning

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An in-depth guide to feature engineering principles and techniques with practical examples in Python.

Feature Engineering

What's inside

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