Feature Engineering 日本語版

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

Introduction

### Course Review and Recommendation: Feature Engineering 日本語版 If you are venturing into the domain of machine learning (ML) and are keen on enhancing the performance of your models, then the course **Feature Engineering 日本語版** on Coursera is a remarkable opportunity. This course focuses on the intricate art of feature engineering using Vertex AI Feature Store and explores various techniques to improve the accuracy of ML models. Below, I breakdown the key aspects of the course, providing a thorough review and a recommendation. #### Course Overview **Feature Engineering 日本語版** offers participants a comprehensive understanding of feature engineering, specifically within the context of Vertex AI Feature Store. The course addresses critical concepts such as identifying effective features from datasets and transforming raw data for optimal model performance. Furthermore, learners will engage with hands-on labs using powerful tools including BigQuery ML, Keras, and TensorFlow. #### Syllabus Breakdown 1. **Introduction:** The initial module sets the stage, outlining the course's objectives and what participants can expect to learn. 2. **Overview of Vertex AI Feature Store:** This module introduces the Vertex AI Feature Store, a vital component that helps manage features across different ML projects. Understanding how to leverage this tool is crucial for any data scientist. 3. **Transforming Raw Data to Features:** One of the most challenging phases of ML development is generating features from raw data. This module emphasizes the qualities of good and bad features and how to represent them effectively in ML models. The course highlights the significance of domain knowledge in this transformation process. 4. **Feature Engineering Techniques:** Participants will learn about the distinctions between machine learning and statistical approaches. This module also covers practical techniques for feature engineering using BigQuery ML and Keras, along with advanced exercises for hands-on experience. 5. **Preprocessing and Feature Creation:** In this segment, the course delves into Apache Beam and Dataflow—technologies that aid in preprocessing and feature engineering. This knowledge is essential for building and executing robust ML workflows. 6. **Feature Crosses - TensorFlow Playground:** Understanding feature crosses is pivotal in modern ML techniques. This module educates participants on identifying problem types that benefit from feature crosses, enhancing the toolkit available to data scientists. 7. **Overview of TensorFlow Transform:** The final module explores the TensorFlow Transform library, which facilitates advanced preprocessing tasks. It covers various use cases, including normalization and input bucketing, thereby showcasing the transformative capabilities of tf.Transform. #### Why You Should Take This Course 1. **Practical Application:** With hands-on labs and real-world applications, you will gain invaluable experience that goes beyond theoretical knowledge. This practical approach enhances your learning experience and prepares you for real-world scenarios. 2. **Expert Guidance:** The content is designed by professionals well-versed in ML and data engineering practices. Participants will benefit from industry insights and best practices that can be applied directly to their projects. 3. **In-Depth Understanding:** This course is perfect for anyone looking to deepen their understanding of feature engineering. It covers both fundamental concepts and advanced techniques, catering to learners at various proficiency levels. 4. **Flexibility:** Being an online course, you can learn at your own pace and revisit materials as needed, fitting your education into your personal schedule. 5. **Language Accessibility:** Conducted in Japanese, it ensures that native speakers can fully engage with the content without language barriers, fostering a better understanding of complex topics. #### Conclusion **Feature Engineering 日本語版** on Coursera is a highly recommended course for individuals interested in elevating their machine learning capabilities through effective feature engineering. By enrolling in this course, you'll not only learn about the technical aspects but also develop the practical skills needed to transform raw data into powerful features that enhance model performance. Whether you are an aspiring data scientist or an experienced ML practitioner, this course promises to deepen your understanding and broaden your skill set in the exciting field of machine learning. Don't miss this opportunity to invest in your professional development!

Syllabus

はじめに

このモジュールでは、コースの概要とその目標を説明します。

このモジュールでは、Vertex AI Feature Store を紹介します。

特徴量エンジニアリングは多くの場合、ML プロジェクトの構築において最も長く、困難なフェーズです。特徴量エンジニアリングのプロセスでは、元データから開始し、独自のドメイン知識を用いて機械学習アルゴリズムを機能させるための特徴を作成します。このモジュールでは、どのような特徴が優れているのか、そして優れた特徴をどのように ML モデルで表現するのかについて確認します。

このモジュールでは、機械学習と統計情報の違いを確認し、BigQuery ML と Keras の両方で特徴量エンジニアリングを実行する方法について説明します。また、高度な特徴量エンジニアリングの演習も行います。

このモジュールでは、Apache Beam を補完する技術である Dataflow について詳しく説明します。Apache Beam と Dataflow 両方とも、前処理や特徴量エンジニアリングを構築して実行するのに役立ちます。

従来の機械学習では、特徴クロスはあまり重要な役割を担っていませんでしたが、最新の機械学習メソッドでは、特徴クロスは非常に有効なツールキットの一部となっています。このモジュールでは、特徴クロスが機械の学習に非常に有益となる問題の種類を認識する方法を確認していきます。

TensorFlow Transform（tf.Transform）は、TensorFlow でデータを前処理するためのライブラリです。tf.Transform は、次のような全走査データを必要とする前処理に便利です。平均値と標準偏差による入力値の正規化、値に対するすべての入力サンプルの確認による語彙の整数値化、観測されたデータの分布に基づく入力のバケット化などです。このモジュールでは、tf.Transform のユースケースを確認します。