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Applying Machine Learning to Your Data with GC - 日本語版

このコースでは、ML について定義し、ビジネスで ML をどのように活用できるのかを学習します。機械学習を使用したデモをいくつか確認し、機械学習の主な用語（インスタンス、特徴、ラベルなど）について学習します。インタラクティブなラボでは、事前トレーニング済みの ML API の呼び出しを実行するほか、BigQuery ML で SQL のみを使用して独自の ML モデルを構築します。

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Syllabus

はじめに

本コースで学習する大まかな内容

ML の概要

このモジュールでは、機械学習について定義し、ビジネスで機械学習をどのように活用できるのかを学習します。ML を使用したデモをいくつか確認し、ML の主な用語（インスタンス、特徴、ラベルなど）について学習します。

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Explores machine learning from the ground up, making it suitable for beginners with no prior knowledge of the subject

Teaches machine learning concepts and techniques that are widely used in business and industry

Provides hands-on experience with real-world data and tools, enabling learners to apply their knowledge immediately

Taught by Google Cloud Training, a recognized leader in the field of machine learning

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Reviews summary

Gcでデータにmlを適用する実践入門

学習者によると、このコースは非常に実践的なアプローチを提供しており、特にBigQuery MLを使ってSQLだけで機械学習モデルを構築できる点が高く評価されています。ハンズオンラボは非常に分かりやすく、実際に手を動かすことで理解が深まり、業務にすぐに活かせると多くの学習者が述べています。また、Google Cloud環境での機械学習の導入として優れた入門コースであり、日本語の説明も丁寧で学習しやすいとの声が多数あります。一方で、すでにMLの経験がある方にとっては内容が物足りない可能性があり、より深い理論的な説明や汎用的なMLスキルを求める学習者には、期待外れになる場合があると一部で指摘されています。

自然で理解しやすい翻訳。

"日本語の説明も非常に丁寧で、違和感なく学習を進められました。"

"教材の質も高く、日本語での提供は本当に助かります。"

Google Cloud上のMLの基礎を学ぶ。

"Google Cloudで機械学習を始めるための優れた入門コースです。"

"ML初心者には非常に分かりやすい構成で、とっつきやすいです。"

"Google Cloud環境での機械学習の導入としてこれ以上ないくらい適切です。"

SQLユーザーにMLアクセスを広げる。

"特に、SQLだけで機械学習モデルを構築できることに驚きました。"

"特にBigQuery MLのセクションは強力で、SQLユーザーにとっては非常にアクセスしやすいでしょう。"

"SQLでMLができるのは本当に便利ですね。"

Google CloudでMLをすぐに適用できる。

"このコースは、BigQuery MLを使ってデータに機械学習を適用する方法について、非常に実践的なアプローチを提供してくれます。ラボは非常に分かりやすく、実際に手を動かすことで理解が深まりました。"

"実務での応用を意識した内容で、非常に役立ちました。ラボ環境もスムーズに動作し、ストレスなく学習できました。"

"特にハンズオンラボは実践的で、すぐに業務に活かせそうだと感じました。"

汎用MLスキルよりツールに焦点。

"BigQuery MLに特化しているため、汎用的なMLスキルを求めていると期待外れになる可能性があります。"

"BigQuery MLに特化しすぎている感があり、一般的な機械学習の知識を深めたい人には向かないかもしれません。"

"実用的な内容で、非常に満足しています。欲を言えば、もう少し発展的なトピック（例えば、カスタムモデルのデプロイやMLOpsの概念）にも触れてほしかったです。"

高度なML理論を深く学べない。

"もう少し深い理論的な説明や、応用例があればさらに良かったと感じます。"

"内容は分かりやすいのですが、すでにMLの経験がある人にとっては物足りないかもしれません。主にツールの使い方に焦点を当てており、アルゴリズムの仕組みについて深く掘り下げることはありません。"

"タイトルから期待していたよりも基礎的な内容でした。"

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 Applying Machine Learning to Your Data with GC - 日本語版 with these activities:

Review previous work in ML

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Refresh your memory on ML concepts and techniques covered in previous courses or experiences, ensuring a solid knowledge base before embarking on this course.

Browse courses on Machine Learning

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Identify relevant materials from past courses or projects
Review the materials, focusing on key concepts and techniques
Take notes or create summaries to reinforce your understanding

Review the basics of data science

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Solidify your understanding of the foundational concepts of data science, preparing you for the more advanced ML concepts in this course.

Browse courses on Data Science

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Review key terms and concepts in data science
Identify different types of data and their characteristics
Practice basic data cleaning and preprocessing techniques
Explore different data visualization techniques

Attend online meetups related to ML

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Connect with other professionals in the ML field, exchange knowledge, and stay up-to-date on industry trends.

Browse courses on Machine Learning

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Identify relevant online meetups using platforms like Meetup or Eventbrite
Attend meetups to listen to presentations, engage in discussions, and network with attendees
Follow up with interesting contacts and explore collaboration opportunities

Five other activities

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Participate in online forums and communities

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Engage with others interested in ML, share your knowledge, and clarify concepts by answering questions and providing support.

Browse courses on Machine Learning

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Identify relevant online forums or communities, such as Stack Overflow or Reddit
Monitor discussions and identify questions that you can answer
Provide thoughtful and informative responses, explaining concepts and sharing resources

Get hands-on experience with ML APIs

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Deepen your understanding of the ML APIs by following guided tutorials to apply them in practice, enhancing your practical skills.

Browse courses on BigQuery ML

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Explore the BigQuery ML API documentation
Follow step-by-step tutorials on calling ML APIs using Cloud Datalab
Experiment with different ML APIs to understand their capabilities
Build sample applications using ML APIs

Solve coding challenges and quizzes

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Sharpen your ML coding abilities and reinforce theoretical concepts through practice and repetition.

Browse courses on Python

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Identify coding challenges and quizzes online or in course materials
Attempt to solve the challenges and quizzes on your own
Compare your solutions with provided answers and identify areas for improvement
Review and practice the concepts related to the problems you solved

Gather resources on advanced ML topics

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Expand your knowledge beyond the course material by compiling a collection of relevant resources on emerging ML topics.

Browse courses on Machine Learning

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Identify advanced ML topics of interest
Search for reputable articles, research papers, and tutorials on these topics
Organize the resources into a central location, such as a shared folder or note-taking app
Periodically review and update the compilation with new and relevant resources

Develop a predictive model using ML

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Apply your ML skills to a real-world problem by building your own predictive model, showcasing your ability to implement ML concepts and solve practical problems.

Browse courses on Machine Learning Model

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Identify a suitable problem and dataset for modeling
Explore different ML algorithms and select an appropriate one
Build the model using BigQuery ML
Evaluate and refine the model
Deploy the model and track its performance

Career center

Learners who complete Applying Machine Learning to Your Data with GC - 日本語版 will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist implements machine learning (ML) algorithms, like those taught in this course, to extract valuable information from large and complex data. Prior knowledge of ML concepts and techniques is crucial to becoming a Data Scientist. The interactive labs in this course help build a foundation in using ML APIs and SQL to construct ML models. This experience can be readily applied to the work of a Data Scientist, and prepares you for more complex projects in the future.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers create, deploy, and maintain ML models, leveraging tools and technologies similar to those taught in this course. A solid understanding of how ML algorithms work and how they can be used to solve business problems, as taught in this course, is vital. By completing this course, you will be able to understand the intent and results of ML models, enabling you to better collaborate on building and deploying ML systems as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts often use ML techniques to identify data trends and patterns, as covered in this course. This course provides a clear understanding of ML terminology and concepts, as well as experience using APIs and building ML models. This foundation is useful to Data Analysts for continuing to learn and apply more advanced techniques in data analysis, such as predictive modeling and anomaly detection.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

Business Intelligence Analysts provide insights that help businesses make informed decisions. This course helps build a foundation for extracting meaningful information from data using ML algorithms, enhancing the skills of a Business Intelligence Analyst. The ability to use SQL to create ML models, as taught in this course, is especially relevant to this role, as it is a widely used tool for data analysis in business intelligence.

See salaries and explore the career path for Business Intelligence Analyst

Software Engineer

Software Engineers may use ML techniques to enhance software applications. This course provides an overview of ML algorithms and hands-on experience using ML APIs, which can be useful for Software Engineers looking to develop or contribute to data-driven applications. While not specific to software engineering, the skills learned in this course can be applied to building and maintaining ML systems.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers often need to understand how ML can enhance products and features. This course covers the fundamentals of ML and its business applications. By understanding the possibilities and limitations of ML, Product Managers can make informed decisions about incorporating ML into their products. The course also provides practical experience using ML APIs, which can be useful for evaluating and selecting ML-based solutions.

See salaries and explore the career path for Product Manager

Marketing Manager

Marketing Managers may use ML to optimize campaigns and personalize customer experiences. This course provides a basic understanding of ML concepts and applications, which can be helpful for Marketing Managers who want to leverage ML to improve marketing strategies. The course also covers using ML APIs, which can be useful for integrating ML-based solutions into marketing campaigns.

See salaries and explore the career path for Marketing Manager

Financial Analyst

Financial Analysts may use ML techniques to analyze financial data and make predictions. This course provides an overview of ML algorithms and their applications in business. By understanding the basics of ML, Financial Analysts can assess the potential of ML for specific financial tasks and make informed decisions about its use.

See salaries and explore the career path for Financial Analyst

Operations Research Analyst

Operations Research Analysts use ML techniques to optimize business processes and systems. This course covers the fundamentals of ML and its business applications. By understanding the potential of ML, Operations Research Analysts can identify opportunities to improve efficiency and effectiveness within organizations.

See salaries and explore the career path for Operations Research Analyst

Quantitative Analyst

Quantitative Analysts use ML techniques to analyze financial data and make predictions. This course provides an overview of ML algorithms and their applications in business. By understanding the fundamentals of ML, Quantitative Analysts can assess the potential of ML for specific financial tasks and make informed decisions about its use.

See salaries and explore the career path for Quantitative Analyst

Market Researcher

Market Researchers may use ML techniques to analyze market data and consumer behavior. This course provides a basic understanding of ML concepts and applications. By understanding the potential of ML, Market Researchers can identify opportunities to use ML to improve market research methods and gain deeper insights into consumer behavior.

See salaries and explore the career path for Market Researcher

Risk Manager

Risk Managers may use ML techniques to identify and assess risks. This course provides an overview of ML algorithms and their applications in business. By understanding the potential of ML, Risk Managers can explore opportunities to use ML to enhance risk management strategies and make more informed decisions.

See salaries and explore the career path for Risk Manager

Actuary

Actuaries may use ML techniques to analyze insurance and financial data. This course provides an overview of ML algorithms and their applications in business. By understanding the fundamentals of ML, Actuaries can assess the potential of ML for specific actuarial tasks and make informed decisions about its use.

See salaries and explore the career path for Actuary

Data Engineer

Data Engineers design and build data pipelines and infrastructure. This course covers using SQL to create ML models. While not a core skill for Data Engineers, this experience can be useful for understanding the data requirements and outputs of ML models, enabling better collaboration with Data Scientists and Machine Learning Engineers.

See salaries and explore the career path for Data Engineer

Database Administrator

Database Administrators manage and maintain databases. This course covers using SQL to create ML models. While not a core skill for Database Administrators, this experience can be useful for understanding the data requirements and outputs of ML models, enabling better support for data-driven applications.

See salaries and explore the career path for Database Administrator

Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Applying Machine Learning to Your Data with GC - 日本語版.

Python и машинное обучение

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Pythonを使用した機械学習の包括的なガイドで、事前トレーニング済みのML APIの呼び出し方法など、コースで扱うトピックに関する詳細情報を提供します。

Applying Machine Learning to Your Data with GC - 日本語版

Two deals to help you save

What's inside

Syllabus

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Save this course

Reviews summary

Gcでデータにmlを適用する実践入門

Activities

Career center

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