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Google Cloud Training
En intégrant le machine learning à des pipelines de données, les entreprises peuvent dégager davantage d'insights de leurs données. Ce cours passera en revue plusieurs façons d'intégrer le machine learning à des pipelines de données sur Google Cloud, selon le niveau de personnalisation requis. Vous découvrirez AutoML pour les cas ne nécessitant que peu de personnalisation (voire aucune), ainsi que Notebooks et BigQuery ML pour les situations qui requièrent des capacités de machine learning plus adaptées. Enfin, vous apprendrez à utiliser des solutions de machine learning en production avec Kubeflow. Les participants mettront en pratique les connaissances qu'ils auront acquises en créant des modèles de machine learning sur Google Cloud à l'aide de Qwiklabs.
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What's inside
Syllabus
Présentation
Dans ce module, nous vous présentons le cours et son déroulement.
Présentation de l'analyse et de l'IA
Ce module présente les options de ML sur Google Cloud.
Read more
Syllabus
Présentation
Dans ce module, nous vous présentons le cours et son déroulement.
Présentation de l'analyse et de l'IA
Ce module présente les options de ML sur Google Cloud.
Read more
API de modèles de ML prédéfinies pour les données non structurées
Ce module s'intéresse principalement à l'utilisation d'API de ML prédéfinies pour vos données non structurées.
Analyse de big data avec Notebooks
Ce module explique comment utiliser Notebooks.
Pipelines de ML de production avec Kubeflow
Ce module explique comment créer des modèles de ML personnalisés, et présente Kubeflow ainsi qu'AI Hub.
Créer un modèle personnalisé avec SQL dans BigQuery ML
Ce module aborde BigQuery ML.
Création d'un modèle personnalisé avec AutoML
Résumé
Ce module récapitule les sujets abordés dans le cours.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Convient aux développeurs et aux ingénieurs des données qui souhaitent intégrer le machine learning dans leurs pipelines de données
Convient aux personnes qui cherchent à améliorer leur compréhension du machine learning
Bon pour les personnes qui souhaitent utiliser les services de machine learning de Google Cloud
Adapté aux personnes qui souhaitent acquérir une expérience pratique du machine learning
Save this course
Save Smart Analytics, Machine Learning, and AI on GCP en Français to your list so you can find it easily later:
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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 Smart Analytics, Machine Learning, and AI on GCP en Français with these
activities:
Prerequisite review
Show steps
Review foundational skills to strengthen your understanding of the course material.
Browse courses on
Machine Learning
Show steps
-
Review basic concepts of machine learning, such as supervised and unsupervised learning.
-
Practice writing simple machine learning algorithms.
Guided tutorials covering ML concepts
Show steps
Supplement your learning by exploring guided tutorials on specific machine learning topics.
Browse courses on
Machine Learning
Show steps
-
Find guided tutorials on topics related to the course, such as data preprocessing, model selection, and model evaluation.
-
Follow the tutorials step-by-step to gain hands-on experience.
Practice exercises on Google Cloud Platform (GCP)
Show steps
Reinforce your understanding by completing practice exercises on GCP.
Browse courses on
Machine Learning
Show steps
-
Set up a GCP account and familiarize yourself with the platform.
-
Complete the Qwiklabs exercises provided in the course to apply your knowledge in a practical setting.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group or discussion forum
Show steps
Engage with peers to exchange knowledge, ask questions, and learn from others' perspectives.
Browse courses on
Machine Learning
Show steps
-
Find a study group or discussion forum related to the course.
-
Participate in discussions, share your insights, and ask for help when needed.
Create a blog post on a specific ML topic
Show steps
Enhance your understanding by explaining a concept or topic related to the course in your own words.
Browse courses on
Machine Learning
Show steps
-
Choose a specific machine learning topic that you are familiar with.
-
Write a blog post that explains the topic in a clear and concise manner.
-
Share your blog post with others for feedback.
Develop a collection of useful resources
Show steps
Curate a collection of resources to supplement your learning and provide future reference.
Browse courses on
Machine Learning
Show steps
-
Identify different types of resources related to the course, such as articles, tutorials, and code samples.
-
Create a central location, such as a digital notebook or online repository, to store and organize your resources.
Mentor a junior learner on ML concepts
Show steps
Enhance your understanding by sharing your knowledge and helping others learn.
Browse courses on
Machine Learning
Show steps
-
Identify a junior learner who is interested in learning about machine learning.
-
Provide guidance and support to help them understand the concepts and apply them in practice.
Prerequisite review
Show steps
Review foundational skills to strengthen your understanding of the course material.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review basic concepts of machine learning, such as supervised and unsupervised learning.
- Practice writing simple machine learning algorithms.
Guided tutorials covering ML concepts
Show steps
Supplement your learning by exploring guided tutorials on specific machine learning topics.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find guided tutorials on topics related to the course, such as data preprocessing, model selection, and model evaluation.
- Follow the tutorials step-by-step to gain hands-on experience.
Practice exercises on Google Cloud Platform (GCP)
Show steps
Reinforce your understanding by completing practice exercises on GCP.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Set up a GCP account and familiarize yourself with the platform.
- Complete the Qwiklabs exercises provided in the course to apply your knowledge in a practical setting.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group or discussion forum
Show steps
Engage with peers to exchange knowledge, ask questions, and learn from others' perspectives.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a study group or discussion forum related to the course.
- Participate in discussions, share your insights, and ask for help when needed.
Create a blog post on a specific ML topic
Show steps
Enhance your understanding by explaining a concept or topic related to the course in your own words.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a specific machine learning topic that you are familiar with.
- Write a blog post that explains the topic in a clear and concise manner.
- Share your blog post with others for feedback.
Develop a collection of useful resources
Show steps
Curate a collection of resources to supplement your learning and provide future reference.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify different types of resources related to the course, such as articles, tutorials, and code samples.
- Create a central location, such as a digital notebook or online repository, to store and organize your resources.
Mentor a junior learner on ML concepts
Show steps
Enhance your understanding by sharing your knowledge and helping others learn.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a junior learner who is interested in learning about machine learning.
- Provide guidance and support to help them understand the concepts and apply them in practice.
Career center
Learners who complete Smart Analytics, Machine Learning, and AI on GCP en Français will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models for various applications. They work on the entire machine learning lifecycle, from data preparation to model evaluation and deployment. This course offers a solid foundation in machine learning on GCP, covering tools like AutoML, Notebooks, BigQuery ML, and Kubeflow. Learners will gain practical experience in building, training, and deploying machine learning models on GCP, which is highly valued by employers in this field.
Data Scientist
Data Scientist
Data Scientists build machine learning models and use their expertise in statistics to solve problems in various industries, such as healthcare, finance, and retail. They work with large datasets, extract insights, and communicate their findings to stakeholders. This course provides a comprehensive overview of machine learning on Google Cloud Platform (GCP), covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow, which are essential tools for Data Scientists. By completing this course, learners will gain hands-on experience in building and deploying machine learning models on GCP, enhancing their skills and making them more competitive in the job market.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use their findings to make recommendations and improve business processes. This course provides a good overview of machine learning on GCP, covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow. By completing this course, learners will gain skills in data analysis and machine learning, which are essential for Data Analysts.
Business Analyst
Business Analyst
Business Analysts use data to identify problems and opportunities for businesses. They work with stakeholders to understand their needs and develop solutions. This course provides a good overview of machine learning on GCP, covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow. By completing this course, learners will gain skills in data analysis and machine learning, which are increasingly important for Business Analysts.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They develop trading strategies and make investment recommendations. This course provides a good overview of machine learning on GCP, covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow. By completing this course, learners will gain skills in data analysis and machine learning, which are essential for Quantitative Analysts.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work on all aspects of the software development process, from requirements gathering to deployment. This course provides a good overview of machine learning on GCP, covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow. By completing this course, learners will gain skills in machine learning, which can enhance their software development capabilities.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that stores and processes data. They work with data scientists and other stakeholders to ensure that data is available and accessible for analysis. This course provides a good overview of machine learning on GCP, covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow. By completing this course, learners will gain skills in data engineering and machine learning, which are increasingly important for Data Engineers.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring products to market. This course provides a good overview of machine learning on GCP, covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow. By completing this course, learners will gain skills in data analysis and machine learning, which are increasingly important for Product Managers.
Management Consultant
Management Consultant
Management Consultants advise businesses on how to improve their operations. They work with clients to identify problems and develop solutions. This course provides a good overview of machine learning on GCP, covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow. By completing this course, learners will gain skills in data analysis and machine learning, which are increasingly important for Management Consultants.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. They work with clients to develop financial plans and manage their investments. This course provides a good overview of machine learning on GCP, covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow. By completing this course, learners will gain skills in data analysis and machine learning, which are increasingly important for Financial Analysts.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work in a variety of industries, such as healthcare, finance, and education. This course provides a good overview of machine learning on GCP, covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow. By completing this course, learners will gain skills in data analysis and machine learning, which are increasingly important for Statisticians.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve problems in a variety of industries, such as healthcare, transportation, and manufacturing. This course provides a good overview of machine learning on GCP, covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow. By completing this course, learners will gain skills in data analysis and machine learning, which are increasingly important for Operations Research Analysts.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks to businesses. They work with management to develop strategies to mitigate risks. This course provides a good overview of machine learning on GCP, covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow. By completing this course, learners will gain skills in data analysis and machine learning, which are increasingly important for Risk Analysts.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. They work in the insurance industry to develop products and pricing strategies. This course provides a good overview of machine learning on GCP, covering topics such as AutoML, Notebooks, BigQuery ML, and Kubeflow. By completing this course, learners will gain skills in data analysis and machine learning, which are increasingly important for Actuaries.
For more career information including salaries, visit:
OpenCourser.com/course/m4pc7e/smart
Reading list
We've selected ten 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
Smart Analytics, Machine Learning, and AI on GCP en Français.
Ce livre fournit une introduction théorique au machine learning. Il couvre les concepts fondamentaux, les algorithmes et les techniques de mise en œuvre.
Save
Ce livre fournit une introduction algorithmique au machine learning. Il couvre les concepts fondamentaux, les algorithmes et les techniques de mise en œuvre.
Ce livre fournit un guide pratique sur l'utilisation de Scikit-Learn, Keras et TensorFlow pour le machine learning. Il couvre les concepts fondamentaux, les algorithmes et les techniques de mise en œuvre.
Ce livre fournit une introduction au deep learning pour le traitement du langage naturel. Il couvre les concepts fondamentaux, les architectures et les techniques de mise en œuvre.
Save
Ce livre fournit une introduction au machine learning pour le traitement audio, vocal et linguistique. Il couvre les concepts fondamentaux, les algorithmes et les techniques de mise en œuvre.
Ce livre fournit une introduction au machine learning pour les soins de santé. Il couvre les concepts fondamentaux, les algorithmes et les techniques de mise en œuvre.
Ce livre fournit une introduction au machine learning pour la finance. Il couvre les concepts fondamentaux, les algorithmes et les techniques de mise en œuvre.
Save
Ce livre fournit une introduction au machine learning pour le marketing. Il couvre les concepts fondamentaux, les algorithmes et les techniques de mise en œuvre.
Ce livre fournit une introduction pratique au deep learning avec Python. Il couvre les concepts fondamentaux, les architectures et les techniques de mise en œuvre.
Ce livre fournit une introduction à la science des données pour les entreprises. Il couvre les concepts fondamentaux, les techniques et les applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/m4pc7e/smart
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Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Français
Transcript
English
This course belongs to a
collection
called
Data Engineer, Big Data and ML on Google Cloud en Français. It's comprised of five courses:
- Google Cloud Big Data and Machine Learning Fundamentals en Français
- Building Resilient Streaming Analytics Systems on GCP en Français
- Smart Analytics, Machine Learning, and AI on GCP en Français (viewing)
- Modernizing Data Lakes and Data Warehouses with GCP en Français
- Building Batch Data Pipelines on GCP en Français
Good to know
Convient aux développeurs et aux ingénieurs des données qui souhaitent intégrer le machine learning dans leurs pipelines de données
Convient aux personnes qui cherchent à améliorer leur compréhension du machine learning
Bon pour les personnes qui souhaitent utiliser les services de machine learning de Google Cloud
Adapté aux personnes qui souhaitent acquérir une expérience pratique du machine learning
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