Feature Engineering

Google Cloud via Coursera

Coursera based on 1,775 ratings

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Overview

Feature Engineering is a crucial course for anyone involved in data science, machine learning, or AI, focusing on the art and science of transforming raw data into meaningful features that improve model performance. This course covers the key techniques and best practices used to extract, select, and create features that help algorithms learn better and make more accurate predictions.You will learn how to handle different types of data—numerical, categorical, text, and time series—and apply feature scaling, encoding, and transformation methods....

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 you build and run preprocessing and feature engineering.

Module 5: Feature Crosses - TensorFlow Playground

In traditional machine learning, feature crosses dont 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 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.