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Google Cloud Training

"Quelles sont les bonnes pratiques pour implémenter le machine learning sur Google Cloud ? En quoi consiste la plate-forme Vertex AI et comment pouvez-vous l'utiliser pour créer, entraîner et déployer rapidement des modèles de machine learning AutoML sans écrire une seule ligne de code ? Qu'est-ce que le machine learning et quels types de problèmes permet-il de résoudre ?

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"Quelles sont les bonnes pratiques pour implémenter le machine learning sur Google Cloud ? En quoi consiste la plate-forme Vertex AI et comment pouvez-vous l'utiliser pour créer, entraîner et déployer rapidement des modèles de machine learning AutoML sans écrire une seule ligne de code ? Qu'est-ce que le machine learning et quels types de problèmes permet-il de résoudre ?

Google aborde le machine learning d'une façon particulière, qui consiste à fournir une plate-forme unifiée pour les ensembles de données gérés, ainsi qu'un magasin de caractéristiques et un moyen de créer, d'entraîner et de déployer des modèles de machine learning sans écrire une seule ligne de code. Il s'agit également de permettre aux utilisateurs d'étiqueter les données et de créer des notebooks Workbench à l'aide de frameworks tels que TensorFlow, Scikit Learn, Pytorch et R. Avec notre plate-forme Vertex AI, il est également possible d'entraîner des modèles personnalisés, de créer des pipelines de composants, ainsi que de générer des prédictions en ligne et par lot. Dans ce cours, nous aborderons les cinq phases permettant de convertir un cas d'utilisation pour le traiter à l'aide du machine learning, avant de déterminer pourquoi chacune d'elles est importante. Nous étudierons également les biais que le machine learning peut amplifier, puis nous apprendrons à les repérer."

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What's inside

Syllabus

Présentation de la série de cours
Ce module vous présente la série de cours et les experts Google qui l'enseigneront.
Qu'est-ce qu'une entreprise axée sur l'IA ?
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Dans ce module, vous allez apprendre à élaborer une stratégie de données basée sur le machine learning.
Google et le machine learning
Ce module présente les connaissances organisationnelles que Google a acquises au fil des années.
Développement du machine learning avec Vertex AI
Le machine learning commence toujours par un objectif, qu'il s'agisse d'un cas d'utilisation professionnel ou pédagogique, ou d'un problème que vous souhaitez résoudre. Ce module explique la phase de démonstration de faisabilité, également appelée phase de tests, qui consiste à déterminer si un modèle est prêt à passer en production.
Développement du machine learning avec des notebooks Vertex
Ce module explore à la fois les notebooks gérés et les notebooks gérés par l'utilisateur pour le développement du machine learning dans Vertex AI.
Bonnes pratiques pour implémenter le machine learning sur Vertex AI
Ce module passe en revue les bonnes pratiques pour les différents processus liés au machine learning dans Vertex AI.
Développement d'IA responsable
Ce module explique pourquoi les systèmes de machine learning ne sont pas justes par défaut et aborde quelques points à retenir lorsque vous intégrez le machine learning à vos produits.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explore les bonnes pratiques pour implémenter le machine learning sur Vertex AI, une plate-forme unifiée pour les ensembles de données gérés, le magasin de caractéristiques et la création, l'entraînement et le déploiement de modèles de machine learning
Enseigne le machine learning à l'aide de Vertex AI, une plate-forme qui permet aux utilisateurs d'étiqueter les données, de créer des notebooks Workbench et de déployer des modèles personnalisés
Développe des compétences en machine learning responsable, essentielles pour créer des systèmes de machine learning équitables et non biaisés
Taught by Google Cloud Training, recognized experts in the field of cloud computing and machine learning
Convient aux débutants et aux professionnels expérimentés qui souhaitent améliorer leurs compétences en machine learning et déployer des modèles sur Vertex AI

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Reviews summary

Informative french study of google ml

This course is an informative and insightful study of Google's machine learning practices taught entirely in French. It is a great resource for those interested in learning more about the nuances of machine learning as implemented by Google.
Valuable Learning Content
"Interesting and right to the point !"
New and Applicable Knowledge
"Excellent ... plein de connaissances nouvelles!"

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 en Français with these activities:
Overview of Machine Learning Concepts
This activity will help you review the essential concepts of machine learning and ensure you have a strong foundation for the course.
Browse courses on Machine Learning
Show steps
  • Review the course description and read the syllabus.
  • Read the introductory chapter of a recommended textbook on machine learning.
  • Watch introductory videos or online tutorials on machine learning basics.
Hands-on Tutorial for Model Development
This activity will provide you with practical experience in developing and training machine learning models.
Show steps
  • Follow the step-by-step tutorial provided by Google Cloud Training.
  • Build and train a simple machine learning model using the provided dataset.
  • Evaluate the performance of your model and iterate to improve it.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
This book provides a comprehensive overview of machine learning techniques and hands-on experience with popular libraries, reinforcing the concepts covered in the course.
Show steps
  • Read selected chapters relevant to the course topics.
  • Work through the practice exercises and examples provided in the book.
  • Apply the concepts to real-world datasets to solidify your understanding.
Two other activities
Expand to see all activities and additional details
Show all five activities
Machine Learning Project Proposal
This activity will allow you to apply your learning to a real-world problem and showcase your understanding of the course concepts.
Show steps
  • Identify a specific machine learning problem to address.
  • Define the project scope, objectives, and deliverables.
  • Propose a methodology and approach to solve the problem.
  • Present your project proposal to the instructor or a peer group for feedback.
Expert Insights on Machine Learning Best Practices
This activity will expose you to industry best practices and insights from experienced machine learning practitioners, enhancing your knowledge and skills.
Show steps
  • Attend live webinars or online workshops hosted by machine learning experts.
  • Follow industry blogs and forums to stay updated on the latest trends and techniques.
  • Engage with online communities and discussion groups to connect with other learners and professionals.

Career center

Learners who complete How Google does Machine Learning en Français will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist is responsible for collecting, analyzing, and interpreting data to help organizations make better decisions. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of machine learning problems, how to choose the right algorithms, and how to evaluate the performance of machine learning models. This course can also help you prepare for the Google Cloud Data Scientist certification.
Machine Learning Engineer
A Machine Learning Engineer is responsible for developing, deploying, and maintaining machine learning models. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different phases of machine learning, how to develop models with Vertex AI, and how to implement best practices for machine learning on Google Cloud. This course can also help you prepare for the Google Cloud Machine Learning Engineer certification.
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to help organizations understand their business better. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of data analysis, how to use machine learning to automate data analysis tasks, and how to communicate your findings to stakeholders. This course can also help you prepare for the Google Cloud Data Analyst certification.
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software applications. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of machine learning algorithms, how to use machine learning to solve software engineering problems, and how to integrate machine learning into your software applications. This course can also help you prepare for the Google Cloud Certified Professional Cloud Architect certification.
Cloud Architect
A Cloud Architect is responsible for designing and managing cloud computing solutions. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of machine learning services that are available on Google Cloud, how to use machine learning to solve cloud computing problems, and how to integrate machine learning into your cloud architecture. This course can also help you prepare for the Google Cloud Certified Professional Cloud Architect certification.
DevOps Engineer
A DevOps Engineer is responsible for bridging the gap between development and operations teams to ensure that software applications are deployed and maintained smoothly. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of machine learning tools and techniques that can be used to automate DevOps tasks, such as continuous integration and delivery, and how to use machine learning to improve the performance and reliability of software applications. This course can also help you prepare for the Google Cloud Certified Professional DevOps Engineer certification.
Risk Analyst
A Risk Analyst is responsible for identifying and managing risks to an organization. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of machine learning techniques that can be used to automate risk analysis tasks, such as fraud detection and credit scoring, and how to use machine learning to improve the accuracy and efficiency of risk management processes. This course can also help you prepare for the Google Cloud Certified Professional Risk Analyst certification.
Business Analyst
A Business Analyst is responsible for analyzing business processes and identifying opportunities for improvement. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of machine learning techniques that can be used to automate business analysis tasks, such as data mining and forecasting, and how to use machine learning to improve the efficiency and effectiveness of business processes. This course can also help you prepare for the Google Cloud Certified Professional Business Analyst certification.
Security Analyst
A Security Analyst is responsible for protecting an organization's information systems from security threats. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of machine learning techniques that can be used to automate security analysis tasks, such as intrusion detection and malware analysis, and how to use machine learning to improve the effectiveness of security measures. This course can also help you prepare for the Google Cloud Certified Professional Security Analyst certification.
Product Manager
A Product Manager is responsible for developing and managing software products. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of machine learning applications, how to use machine learning to solve product management problems, and how to integrate machine learning into your software products. This course can also help you prepare for the Google Cloud Certified Professional Product Manager certification.
Quantitative Analyst
A Quantitative Analyst is responsible for using mathematical and statistical models to analyze financial data and make investment decisions. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of machine learning algorithms that can be used to solve financial problems, such as portfolio optimization and risk management, and how to use machine learning to improve the performance of investment portfolios. This course can also help you prepare for the Google Cloud Certified Professional Quantitative Analyst certification.
Machine Learning Researcher
A Machine Learning Researcher is responsible for developing new machine learning algorithms and techniques. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of machine learning problems, how to choose the right algorithms, and how to evaluate the performance of machine learning models. This course can also help you prepare for a PhD in machine learning or a related field.
Data Engineer
A Data Engineer is responsible for designing and building data pipelines that collect, process, and store data. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of machine learning techniques that can be used to automate data engineering tasks, such as data cleansing and data transformation, and how to use machine learning to improve the efficiency and accuracy of data pipelines. This course can also help you prepare for the Google Cloud Certified Professional Data Engineer certification.
Artificial Intelligence Engineer
An Artificial Intelligence Engineer is responsible for designing and developing artificial intelligence systems. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of artificial intelligence algorithms, how to choose the right algorithms, and how to evaluate the performance of artificial intelligence systems. This course can also help you prepare for a PhD in artificial intelligence or a related field.
Deep Learning Engineer
A Deep Learning Engineer is responsible for designing and developing deep learning models. This course can help you build a foundation in machine learning and gain the skills you need to succeed in this role. You will learn about the different types of deep learning algorithms, how to choose the right algorithms, and how to evaluate the performance of deep learning models. This course can also help you prepare for a PhD in deep learning or a related field.

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 How Google does Machine Learning en Français.
As the foundational textbook for deep learning, this book offers a comprehensive and authoritative treatment of the subject. It provides a thorough understanding of deep learning models, architectures, and algorithms, making it essential reading for anyone interested in the field.
This comprehensive textbook covers a wide range of machine learning topics, including supervised and unsupervised learning, Bayesian methods, and statistical modeling. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive and rigorous treatment of machine learning from a probabilistic perspective. It valuable resource for researchers and advanced practitioners interested in the theoretical foundations of machine learning.
Provides a comprehensive treatment of statistical learning methods with a focus on sparsity. It valuable resource for researchers and advanced practitioners interested in the theoretical and algorithmic aspects of machine learning.
This practical guide covers the essential concepts and techniques of machine learning using popular libraries like Scikit-Learn, Keras, and TensorFlow. With hands-on exercises and real-world examples, it provides a comprehensive and accessible introduction to machine learning implementation.
Provides a comprehensive and practical guide to deep learning using Python. With a focus on hands-on implementation, it valuable resource for those interested in applying deep learning techniques to real-world problems.
This classic textbook provides a comprehensive introduction to reinforcement learning, covering the fundamental concepts, algorithms, and applications. It valuable resource for both beginners and experienced researchers in the field.
This practical guide provides a hands-on approach to machine learning, focusing on the practical implementation of algorithms. It good reference for those interested in applying machine learning techniques to real-world problems.
This practical guide introduces machine learning concepts and techniques to a non-technical audience. With a focus on real-world applications, it provides valuable insights for those looking to leverage machine learning in their projects.
This introductory textbook covers a wide range of machine learning and data mining concepts, providing a good foundation for beginners. It is useful for those seeking a general overview of the field.
This concise and accessible book provides a quick overview of machine learning concepts and algorithms. It good starting point for beginners who want to get a basic understanding of the field.

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