Google Cloud Big Data and Machine Learning Fundamentals 日本語版

Go to Course: https://www.coursera.org/learn/gcp-big-data-ml-fundamentals-jp

Introduction

### Course Review: Google Cloud Big Data and Machine Learning Fundamentals 日本語版 on Coursera If you're looking to enhance your understanding of big data and machine learning using Google Cloud, the course titled **"Google Cloud Big Data and Machine Learning Fundamentals 日本語版"** is an excellent choice. Designed for professionals and enthusiasts alike, this course offers a comprehensive overview of the key products and services that Google Cloud provides to support the lifecycle of data to AI. #### Overview This course introduces you to the various facets of big data and machine learning on Google Cloud, focusing on the integration of these technologies to create robust data pipelines and machine learning models. You will learn how to utilize Vertex AI, Google Cloud's unified platform for building and managing ML projects, which serves as a springboard for transitioning from large datasets to actionable AI outcomes. #### Syllabus Breakdown 1. **Course Overview:** The course begins with a clear outline of its structure and goals, setting the stage for what you will learn throughout the modules. 2. **Big Data and Machine Learning on Google Cloud:** This section delves into the critical components of Google Cloud infrastructure, shedding light on the various products and services available to support the lifecycle of data to AI. 3. **Streaming Data Engineering:** Here, the course introduces Google Cloud solutions for managing streaming data. You'll gain insights into end-to-end pipelines that involve data ingestion using Pub/Sub, data processing with Dataflow, and data visualization with Looker and Data Studio. 4. **BigQuery for Big Data:** This section focuses on BigQuery, the fully-managed serverless data warehouse by Google. You will learn about BigQuery ML and how to build custom machine learning models, delving into the essential commands used in the process. 5. **Machine Learning Options on Google Cloud:** You'll explore four options for building machine learning models on Google Cloud. This section emphasizes Vertex AI, an integrated platform that simplifies the lifecycle management of ML projects. 6. **Machine Learning Workflow with Vertex AI:** The course covers the three main phases of the machine learning workflow in Vertex AI: data preparation, model training, and model deployment. You will have the opportunity to build machine learning models using AutoML, providing practical experience in the process. 7. **Course Summary:** Finally, the course wraps up by reviewing the learned topics and providing additional resources for further deepening your understanding. #### Recommendation This course is highly recommended for anyone who is interested in big data and machine learning with a particular focus on Google Cloud technologies. Its well-structured syllabus guides learners from foundational concepts to practical applications, making it ideal for both newcomers and seasoned professionals wanting to enhance their skills. The use of real-world applications and tools, such as Vertex AI and BigQuery, offers invaluable hands-on experience that is essential in today’s data-driven job market. Moreover, the course is available in Japanese, making it accessible for Japanese speakers who prefer to learn in their native language. Completing this course will not only bolster your knowledge but also significantly enhance your employability in roles related to data science, machine learning, and cloud computing. Whether you're seeking to expand your current skill set or pivot into a new career path in tech, this course offers the necessary foundation to succeed. In summary, if you're ready to embark on a journey through the exciting fields of big data and machine learning, look no further than **"Google Cloud Big Data and Machine Learning Fundamentals 日本語版"**. Sign up today and take your first step toward mastering these essential technologies!

Syllabus

コースの概要

このセクションでは、Big Data and Machine Learning Fundamentals コースの受講者向けにコースの構成と目標をおおまかに説明します。

このセクションでは、Google Cloud のインフラストラクチャの主要コンポーネントについて説明します。Google Cloud におけるデータから AI へのライフサイクルをサポートする、ビッグデータと機械学習のさまざまなプロダクトおよびサービスを紹介します。

このセクションでは、ストリーミング データを管理するための Google Cloud ソリューションを紹介します。また、エンドツーエンドのパイプライン（Pub/Sub を使用したデータの取り込み、Dataflow を使用したデータ処理、Looker とデータポータルを使用したデータのビジュアライゼーションなど）について詳しく説明します。

このセクションでは、Google が提供するフルマネージドのサーバーレス データ ウェアハウスである BigQuery について紹介します。BigQuery ML と、カスタム機械学習モデルの構築に使用するプロセスおよび主なコマンドについても説明します。

このセクションでは、Google Cloud で機械学習モデルを構築するための 4 つのオプションについて説明します。また、ML プロジェクトのライフサイクルを構築、管理するための Google 統合プラットフォームである Vertex AI についても紹介します。

このセクションでは、Vertex AI の機械学習ワークフローにおける 3 つの主なフェーズ（データの準備、モデルのトレーニング、モデルの準備）を中心に説明します。実際に AutoML を使用して機械学習モデルを構築することができます。

このセクションでは、コースで学習したトピックを復習し、理解を深めるための追加のリソースを提供しています。