Feature Engineering from Coursera

What's inside

Syllabus

Module 0: Introduction

This module provides an overview of the course and its objectives.

Module 1: Introduction to Vertex AI Feature Store

This module introduces Vertex AI Feature Store.

Module 2: Raw Data to Features

Feature engineering is often the longest and most difficult phase of building your ML project. In the feature engineering process, you start with your raw data and use your own domain knowledge to create features that will make your machine learning algorithms work. In this module we explore what makes a good feature and how to represent them in your ML model.

Module 3: Feature Engineering

This module reviews the differences between machine learning and statistics, and how to perform feature engineering in both BigQuery ML and Keras. We'll also cover some advanced feature engineering practices.

Module 4: Preprocessing and Feature Creation

In this module you will learn more about Dataflow, which is a complementary technology to Apache Beam and both of them can help you build and run preprocessing and feature engineering.

Module 5: Feature Crosses - TensorFlow Playground

In traditional machine learning, feature crosses don’t play much of a role, but in modern day ML methods, feature crosses are an invaluable part of your toolkit. In this module, you will learn how to recognize the kinds of problems where feature crosses are a powerful way to help machines learn.

Module 6: Introduction to TensorFlow Transform

TensorFlow Transform (tf.Transform) is a library for preprocessing data with TensorFlow. tf.Transform is useful for preprocessing that requires a full pass the data, such as: - normalizing an input value by mean and stdev - integerizing a vocabulary by looking at all input examples for values - bucketizing inputs based on the observed data distribution In this module we will explore use cases for tf.Transform.

Module 7: Summary

This module is a summary of the Feature Engineering course.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches methods of feature engineering, a standard process for improving machine learning models

The instructors, Google Cloud Training, are experts in Google's cloud-based machine learning platforms, including Vertex AI

Provides hands-on labs for practicing concepts and testing models

Covers advanced techniques including feature crosses and TensorFlow Transform for feature engineering

Introduces industry-standard techniques used by professional data engineers in building machine learning models

Requires familiarity with Python and basic machine learning concepts

Reviews summary

Informative feature engineering

Learners say that Feature Engineering is a largely positive course that provides engaging assignments and difficult exams. Students highlight the course's in-depth coverage of feature engineering and its practical applications, making it a valuable resource for those interested in machine learning. Despite some difficult exams and outdated labs, learners found the course to be largely positive and recommend it to others.

Includes practical examples and hands-on assignments to reinforce learning.

"Really Good! I learnt so much! I'm really thankful for having such a great teaching team. thank you!"

"The course was very informative, but I think that there are opportunities for the student to have to figure out how to do more portions of the labs."

Provides thorough explanations of feature engineering concepts and techniques.

"This course covers a lot about the data pre-processing, and the tools available in Google Cloud to enable the gruelling tasks."

"In depth and advanced. I spent hours poring over the Jupyter notebooks and consequently derived a great deal of value from the course."

Some labs may be outdated or have technical issues.

"A lot of the labs need updating and revising and made more meaningful."

"Many installations on the pylab notebooks are broken."

Exams can be challenging, requiring a solid understanding of the material.

"This course is a little bit harder than the former three courses."

"The last three sections of this course are very difficult."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Feature Engineering with these activities:

Review basic data manipulation

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Review basic data manipulation and cleaning techniques to ensure a solid foundation for feature engineering.

Browse courses on Data Cleaning

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Review data normalization techniques
Practice cleaning and filtering data using Python or R

Build a simple machine learning model using a dataset

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Apply feature engineering techniques to improve the accuracy of a machine learning model.

Browse courses on Machine Learning Models

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Choose a dataset and define a machine learning task
Apply feature engineering techniques to the dataset
Train and evaluate the machine learning model

Explore use cases of Vertex AI Feature Store

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Deepen understanding of Vertex AI Feature Store and its applications in real-world scenarios.

Browse courses on Vertex AI

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Follow online tutorials and documentation to learn about Vertex AI Feature Store
Explore case studies and examples of using Feature Store in practice

Two other activities

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Show all five activities

Read 'Feature Engineering for Machine Learning' by Alice Zheng

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Expand knowledge of feature engineering principles and techniques through reading a recommended book.

View Feature Engineering for Machine Learning:... on Amazon

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Read selected chapters or sections from the book
Take notes and summarize key concepts

Solve feature engineering practice problems

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Reinforce feature engineering skills by solving practice problems.

Browse courses on Feature Engineering

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Find practice problems online or in textbooks
Attempt to solve the problems independently
Review solutions and learn from mistakes

Career center

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

Data Scientist

Data Scientists are experts in using data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They then use this information to build predictive models and make recommendations to businesses. A course on feature engineering can help Data Scientists improve the accuracy of their models and find which data columns make the most useful features. This can lead to better decision-making and improved business outcomes.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for building and deploying machine learning models. They work with Data Scientists to identify the right data and features to use, and then they develop and train the models. A course on feature engineering can help Machine Learning Engineers build more accurate and efficient models. This can lead to better business outcomes and a competitive advantage.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data. They use this information to identify trends and patterns, and then they report their findings to businesses. A course on feature engineering can help Data Analysts improve the quality of their data and make more accurate predictions. This can lead to better decision-making and improved business outcomes.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts are responsible for understanding the needs of a business and then developing solutions to meet those needs. They work with stakeholders to gather requirements, analyze data, and make recommendations. A course on feature engineering can help Business Analysts better understand the data that is available to them and make more informed decisions. This can lead to better solutions and improved business outcomes.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers are responsible for designing, developing, and maintaining software applications. They work with stakeholders to gather requirements, design the application architecture, and write the code. A course on feature engineering can help Software Engineers build more efficient and effective applications. This can lead to better user experiences and improved business outcomes.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers are responsible for the overall success of a product. They work with stakeholders to define the product vision, develop the product roadmap, and launch the product to market. A course on feature engineering can help Product Managers better understand the technical aspects of product development and make more informed decisions. This can lead to better products and improved business outcomes.

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Quantitative Analyst

Quantitative Analysts are responsible for using mathematical and statistical models to analyze data and make predictions. They work with stakeholders to identify the right data and features to use, and then they develop and validate the models. A course on feature engineering can help Quantitative Analysts build more accurate and efficient models. This can lead to better decision-making and improved business outcomes.

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Statistician

Statisticians are responsible for collecting, analyzing, and interpreting data. They use this information to make inferences about the world around us. A course on feature engineering can help Statisticians improve the quality of their data and make more accurate predictions. This can lead to better decision-making and improved business outcomes.

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Data Engineer

Data Engineers are responsible for building and maintaining the infrastructure that is used to store and process data. They work with Data Scientists and Machine Learning Engineers to ensure that the data is available and accessible. A course on feature engineering can help Data Engineers build more efficient and effective data pipelines. This can lead to better data quality and improved business outcomes.

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Database Administrator

Database Administrators are responsible for managing and maintaining databases. They ensure that the data is secure and accessible to users. A course on feature engineering can help Database Administrators better understand the data that is stored in their databases and make more informed decisions about how to manage it. This can lead to improved data quality and improved business outcomes.

See salaries and explore the career path for Database Administrator

Information Architect

Information Architects are responsible for designing and managing the information architecture of an organization. They work with stakeholders to understand the organization's information needs and then develop a plan for how to meet those needs. A course on feature engineering can help Information Architects better understand the data that is available to them and how to make it more accessible to users. This can lead to improved decision-making and improved business outcomes.

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Data Governance Analyst

Data Governance Analysts are responsible for developing and implementing data governance policies and procedures. They work with stakeholders to ensure that the data is used in a consistent and ethical manner. A course on feature engineering can help Data Governance Analysts better understand the data that is available to them and how to make it more accessible to users. This can lead to improved data quality and improved business outcomes.

See salaries and explore the career path for Data Governance Analyst

Data Privacy Analyst

Data Privacy Analysts are responsible for ensuring that an organization's data is used in a compliant manner. They work with stakeholders to identify and mitigate data privacy risks. A course on feature engineering can help Data Privacy Analysts better understand the data that is available to them and how to make it more secure. This can lead to improved data privacy and improved business outcomes.

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Information Security Analyst

Information Security Analysts are responsible for protecting an organization's data from unauthorized access. They work with stakeholders to identify and mitigate security risks. A course on feature engineering can help Information Security Analysts better understand the data that is available to them and how to make it more secure. This can lead to improved data security and improved business outcomes.

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Software Architect

Software Architects are responsible for designing and developing the overall architecture of a software application. They work with stakeholders to understand the application's requirements and then design the system that will meet those requirements. A course on feature engineering can help Software Architects better understand the data that is needed by the application and how to make it available to the application's users. This can lead to better system performance and improved business outcomes.

See salaries and explore the career path for Software Architect