Feature Engineering

Go to Course: https://www.coursera.org/learn/feature-engineering

Introduction

**Course Review: Feature Engineering on Coursera** **Overview:** In the rapidly evolving field of data science and machine learning, the importance of feature engineering cannot be overstated. The “Feature Engineering” course on Coursera beautifully captures this essence, providing learners with both theoretical knowledge and practical skills relevant to building robust machine learning models. Throughout this course, participants will gain insights into the Vertex AI Feature Store, leverage powerful tools like BigQuery ML, Keras, and TensorFlow, and master the intricacies of transforming raw data into impactful features. **Course Structure:** The course is structured into seven comprehensive modules, each designed to provide a deep dive into specific aspects of feature engineering: 1. **Module 0: Introduction** The course kicks off with an overview of its objectives, setting a solid foundation for newcomers who might be unfamiliar with feature engineering concepts. 2. **Module 1: Introduction to Vertex AI Feature Store** This module introduces learners to Vertex AI Feature Store, demonstrating its significance in managing features and the benefits it brings to machine learning workflows. 3. **Module 2: Raw Data to Features** Delving into the complexities of feature engineering, this module emphasizes the importance of domain knowledge and how to derive effective features from raw data. Learners will understand the qualities of good features and their representation in ML models. 4. **Module 3: Feature Engineering** This module provides an in-depth comparison of machine learning and statistics, offering practical guidance on performing feature engineering in BigQuery ML and Keras, along with advanced feature engineering practices that can enhance model performance. 5. **Module 4: Preprocessing and Feature Creation** Participants will explore Dataflow, a powerful tool that complements Apache Beam, as they learn to build and execute effective preprocessing and feature engineering processes. 6. **Module 5: Feature Crosses - TensorFlow Playground** Understanding feature crosses is pivotal in modern machine learning. This module helps learners recognize scenarios where feature crosses can significantly enhance model learning and accuracy. 7. **Module 6: Introduction to TensorFlow Transform** Focusing on preprocessing data using TensorFlow, this module investigates TensorFlow Transform (tf.Transform), teaching learners about essential preprocessing techniques like normalization, vocabulary integerization, and bucketization based on data distribution. 8. **Module 7: Summary** The final module revisits the core concepts discussed throughout the course, reinforcing key takeaways and ensuring participants are well-prepared to apply their newfound knowledge in real-world scenarios. **Review:** The “Feature Engineering” course delivers high-quality content that is both engaging and informative. The combination of theory with hands-on labs enables learners to apply what they’ve learned in real-time, solidifying their understanding. The course is well-structured, with each module building upon the last, making it easy for participants to follow along and grasp complex concepts. Furthermore, the use of contemporary tools like Vertex AI, BigQuery ML, Keras, and TensorFlow not only equips learners with essential skills but also ensures they are aligned with current industry standards. The course progressively explores more advanced topics, catering to both beginners and those with some experience in machine learning, thus appealing to a broad audience. **Recommendation:** Whether you are a data analyst looking to enhance your machine learning capabilities, a beginner venturing into the domain of data science, or an experienced professional wishing to deepen your feature engineering knowledge, this course comes highly recommended. Its practical labs, expert instruction, and comprehensive coverage of critical topics make it a worthwhile investment in your professional development. Enroll in the “Feature Engineering” course on Coursera today and take your first step towards mastering one of the most crucial facets of machine learning!

Syllabus

Module 0: Introduction

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

This module introduces Vertex AI Feature Store.

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.

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.

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.

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.

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.

This module is a summary of the Feature Engineering course.