How Google does Machine Learning 日本語版 from Coursera

"Google Cloud で機械学習を実装する際のベストプラクティスには何があるでしょうか。Vertex AI とは何であり、このプラットフォームを使用してコードを 1 行も記述せずに AutoML 機械学習モデルを迅速に構築、トレーニング、デプロイするにはどうすればよいでしょうか。機械学習とはどのようなもので、どのような問題の解決に役立つのでしょうか。

Google では機械学習について独自の視点で考えています。マネージドデータセット、特徴量ストア、そしてコードを 1 行も記述せずに迅速に機械学習モデルを構築、トレーニング、デプロイする手段を 1 つにまとめた統合プラットフォームを提供するとともに、データにラベル付けし、TensorFlow、SciKit Learn、Pytorch、R やその他のフレームワークを使用して Workbench ノートブックを作成できるようにすることが、Google の考える機械学習の在り方です。Google の Vertex AI プラットフォームでは、カスタムモデルをトレーニングしたり、コンポーネントパイプラインを構築したりすることもできます。さらに、オンライン予測とバッチ予測の両方を実施できます。このコースでは、候補となるユースケースを機械学習で学習できる形に変換する 5 つのフェーズについても説明し、これらのフェーズを省略しないことが重要である理由について論じます。最後に、機械学習によって増幅される可能性のあるバイアスの認識と、それを識別する方法について説明します。"

What's inside

Syllabus

コースの概要

この専門講座の概要と、講座を担当する Google のエキスパートを紹介します。

AI ファーストとは

Google が自社の企業戦略は AI ファーストであると説明する際の意味と、実際の意味について学びます。

Google の ML の取り組み

このモジュールでは、Google が長年にわたって蓄積してきた組織としてのノウハウについて説明します。

インクルーシブ ML

このモジュールでは、機械学習システムがデフォルトでは公平でない理由と、機械学習をプロダクトに導入する際に留意しなければならないいくつかの事項について説明します。

クラウドの Python ノートブック

AI Platform Notebooks の役割を理解する

まとめ

この専門講座で取り上げる機械学習の主なトピックを確認します。

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops skills and knowledge relevant to the software industry, such as machine learning and data science

Taught by Google Cloud Training, an organization recognized for their work in machine learning and data science

Covers topics such as machine learning best practices, Vertex AI, and AutoML, which are highly relevant to industry

Provides a comprehensive study of machine learning, including its applications and potential biases

Explores the five phases of transforming candidate use cases into machine learning, emphasizing the importance of each phase

Requires experience with machine learning concepts and Python programming, which may be a barrier for some learners

Reviews summary

Google ml: primer in best practices

This course offers primer on best practices for implementing machine learning at Google Cloud. Topics covered include an overview of Vertex AI, Google's approach to machine learning, inclusive ML, and the use of notebooks in the cloud.

Provides fast ML model building without writing code.

"Google’s Vertex AI platform allows you to train custom models or build component pipelines."

Course draws on Google's extensive experience in machine learning.

"This module illustrates the institutional knowledge Google has accrued..."

Instructor shares practical knowledge of what is important.

"...the lecture was very informative, speaking from experience what is important."

Some Quicklabs did not work correctly.

"...the explanation on how to use quicklabs was hard to follow."

"...Some of the qwik labs were not working properly..."

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 How Google does Machine Learning 日本語版 with these activities:

Attend a workshop on machine learning for beginners

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Gain a foundational understanding of machine learning principles and techniques in a structured environment.

Browse courses on Machine Learning

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Find a workshop that fits your interests
Attend the workshop and actively participate
Apply what you learned in your own projects

Review your notes and assignments from previous machine learning courses

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Refresh your memory on key concepts and techniques in machine learning.

Browse courses on Machine Learning

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Go through your notes and assignments
Identify areas where you need additional review
Re-read relevant sections of your textbooks or online resources

Complete the Google Cloud Machine Learning Crash Course

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Reinforce your knowledge of machine learning fundamentals and Google Cloud Platform.

Browse courses on Machine Learning

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Watch the video lectures
Complete the hands-on labs
Take the quizzes

Four other activities

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Show all seven activities

Build an AutoML machine learning model using Vertex AI

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Build a real-world machine learning model to solidify understanding of the concepts covered in the course.

Browse courses on Vertex AI

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Choose a dataset for your model
Select the appropriate AutoML type for your dataset
Train and deploy your model using Vertex AI
Evaluate your model's performance

Follow the official Google Cloud Machine Learning tutorials

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Develop practical skills in using Google Cloud Machine Learning services by following official tutorials.

Browse courses on Machine Learning

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Choose a tutorial that aligns with your interests
Follow the tutorial instructions carefully
Experiment with the code and try out different scenarios

Tutor other students in machine learning concepts

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Solidify your knowledge by explaining concepts to others, identifying areas where you need further understanding.

Browse courses on Machine Learning

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Identify students who need help with machine learning
Prepare your lessons and materials
Tutor students and answer their questions

Build a machine learning model to solve a real-world problem

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Apply your machine learning skills to a practical problem, deepening your understanding and developing valuable experience.

Browse courses on Machine Learning

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Define the problem you want to solve
Gather and prepare your data
Choose and train a machine learning model
Deploy and evaluate your model

Career center

Learners who complete How Google does Machine Learning 日本語版 will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer is a software engineer who specializes in the implementation and maintenance of machine learning systems. This course provides a foundation in machine learning and helps build the skills that are in high demand in the field today, including best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course introduces the principles of machine learning, a key component of data science, and shows how to apply machine learning techniques to real-world data problems. This course is particularly valuable for Data Scientists because it provides a foundation in machine learning and helps build the skills that are in high demand in the field today.

See salaries and explore the career path for Data Scientist

Software Engineer

A Software Engineer designs, develops, tests, and maintains software systems. This course provides a foundation in machine learning, a key technology for modern software systems, and helps build the skills that are in high demand in the field today, such as best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Software Engineer

Data Analyst

A Data Analyst analyzes data to identify trends and patterns, and to make predictions about future outcomes. This course provides a foundation in machine learning, a key tool for data analysis, and helps build the skills that are in high demand in the field today, including best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Data Analyst

Product Manager

A Product Manager is responsible for the development and launch of new products and features. This course provides a foundation in machine learning, a key technology for modern products, and helps build the skills that are in high demand in the field today, such as best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Product Manager

Business Analyst

A Business Analyst is responsible for analyzing business processes and identifying opportunities for improvement. This course provides a foundation in machine learning, a key tool for business analysis, and helps build the skills that are in high demand in the field today, such as best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Business Analyst

Data Engineer

A Data Engineer is responsible for designing, building, and maintaining data pipelines. This course provides a foundation in machine learning, a key technology for data engineering, and helps build the skills that are in high demand in the field today, such as best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical techniques to analyze financial data and make investment decisions. This course provides a foundation in machine learning, a key tool for quantitative analysis, and helps build the skills that are in high demand in the field today, such as best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Quantitative Analyst

Actuary

An Actuary is responsible for analyzing risks and identifying opportunities to mitigate them. This course provides a foundation in machine learning, a key tool for actuarial science, and helps build the skills that are in high demand in the field today, such as best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Actuary

Sales Analyst

A Sales Analyst is responsible for analyzing sales data to identify trends and patterns, and to make predictions about future sales. This course provides a foundation in machine learning, a key tool for sales analysis, and helps build the skills that are in high demand in the field today, such as best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Sales Analyst

Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical techniques to analyze operations and identify opportunities for improvement. This course provides a foundation in machine learning, a key tool for operations research, and helps build the skills that are in high demand in the field today, such as best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Operations Research Analyst

Statistician

A Statistician is responsible for collecting, analyzing, and interpreting data. This course provides a foundation in machine learning, a key tool for statistics, and helps build the skills that are in high demand in the field today, such as best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Statistician

Financial Analyst

A Financial Analyst is responsible for analyzing financial data to make investment decisions. This course provides a foundation in machine learning, a key tool for financial analysis, and helps build the skills that are in high demand in the field today, such as best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

A Marketing Analyst is responsible for analyzing marketing data to identify trends and patterns, and to make predictions about future outcomes. This course provides a foundation in machine learning, a key tool for marketing analysis, and helps build the skills that are in high demand in the field today, such as best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Marketing Analyst

Risk Analyst

A Risk Analyst is responsible for analyzing risks and identifying opportunities to mitigate them. This course provides a foundation in machine learning, a key tool for risk analysis, and helps build the skills that are in high demand in the field today, such as best practices for implementing machine learning in the cloud and using tools like Vertex AI to build, train, and deploy machine learning models.

See salaries and explore the career path for Risk Analyst

How Google does Machine Learning 日本語版

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Reviews summary

Google ml: primer in best practices

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