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Google Cloud Training

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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What's inside

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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事前トレーニング済み ML API
このモジュールでは、Cloud Datalab 内で利用可能な事前構築済み、事前トレーニング済みの機械学習モデル(画像認識や感情分析など)について詳細を学習します。
BigQuery で ML データセットを作成する
BigQuery で ML データセットを作成する方法について学習します。
BigQuery で ML モデルを作成する
このモジュールでは、BigQuery 内で直接機械学習モデルを構築する方法を学習します。新しい構文を学習し、ML モデルの構築、評価、テストのフェーズを詳しく確認します。
コース終了にあたってのまとめ
これで終了です。コースで学習した内容を復習し、学習を継続するために利用できるリソースを確認しましょう。

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
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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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
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
Show steps
  • 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
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
Show steps
  • 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
Connect with other professionals in the ML field, exchange knowledge, and stay up-to-date on industry trends.
Browse courses on Machine Learning
Show steps
  • 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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Show all eight activities
Participate in online forums and communities
Engage with others interested in ML, share your knowledge, and clarify concepts by answering questions and providing support.
Browse courses on Machine Learning
Show steps
  • 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
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
Show steps
  • 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
Sharpen your ML coding abilities and reinforce theoretical concepts through practice and repetition.
Browse courses on Python
Show steps
  • 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
Expand your knowledge beyond the course material by compiling a collection of relevant resources on emerging ML topics.
Browse courses on Machine Learning
Show steps
  • 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
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
Show steps
  • 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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。
機械学習の基礎と数学的原理を簡潔かつ分かりやすく解説した入門書です。このコースで学ぶ概念を補強するために役立ちます。
ディープラーニングの包括的なテキストで、このコースで扱うトピックに関する詳細な情報を提供します。
機械学習の包括的な概要を提供する教科書で、このコースで扱う概念のより深い理解を得るのに役立ちます。
BigQuery MLを使用して機械学習モデルを構築するためのレシピ集で、このコースで扱うトピックに関する実践的なガイダンスを提供します。
ビジネスにおけるデータサイエンスの適用について説明する教科書で、このコースで扱う概念のビジネス上の関連性を提供します。
強化学習の包括的なガイドで、このコースで扱うトピックに関する追加の背景知識を提供します。

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