We may earn an affiliate commission when you visit our partners.

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

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。

Enroll now

Or subscribe to Coursera Plus

And get unlimited access to Coursera

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。

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

Save this course

Save Applying Machine Learning to Your Data with GC - 日本語版 to your list so you can find it easily later:

Save

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

Show steps

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

Show steps

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

Show steps

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

Expand to see all activities and additional details

Show all eight activities

Participate in online forums and communities

Show steps

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

Show steps

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

Show steps

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

Show steps

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

Show steps

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.

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

Save

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

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

Share

Similar courses