Serverless Machine Learning with Tensorflow on Google Cloud Platform en Français from Coursera

Ce cours à la demande accéléré réparti sur une semaine propose aux participants une introduction pratique à la conception et à la création de modèles de machine learning sur la plateforme Google Cloud. Grâce à une combinaison de présentations, de démonstrations et d'ateliers pratiques, les participants découvriront les concepts du machine learning, aussi appelé apprentissage automatique, et de TensorFlow, et acquerront des compétences pratiques pour développer, évaluer et produire des modèles de machine learning. OBJECTIFS Ce cours enseigne aux participants les compétences suivantes : ● Identifier les cas d'utilisation de machine learning ● Élaborer un modèle de ML à l'aide de TensorFlow ● Élaborer des modèles de ML évolutifs et déployables avec Cloud ML ● Comprendre l'importance des fonctionnalités de prétraitement et de combinaison ● Incorporer des concepts de ML évolués à leurs modèles ● Faire passer des modèles de ML entraînés en production CONDITIONS PRÉALABLES Pour tirer le meilleur parti de ce cours, les participants doivent : ● avoir achevé le cours "Google Cloud Fundamentals: Big Data & Machine Learning" OU posséder une expérience équivalente ; ● posséder des compétences de base dans un langage de requête courant tel que SQL ; ● posséder une expérience des activités de modélisation, d'extraction, de transformation et de chargement de données ; ● développer des applications à l'aide d'un langage de programmation commun tel que Python ; ● connaître le machine learning et/ou les statistiques. Remarque concernant les comptes Google : • Les services Google sont actuellement indisponibles en Chine.

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Know what's good

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Provides essential skills in TensorFlow, foundational for ML development

Offers hands-on training in Cloud ML, industry standard for deployment

Excellent option for those seeking technical expertise in ML

Fits well with ML enthusiasts and professionals seeking advanced skills

Assumes some prior understanding in ML and programming

Requires completion of prerequisites or equivalent experience

Reviews summary

Ml with tensorflow on google cloud

This course introduces participants to machine learning concepts and Tensorflow, teaching them how to develop and deploy models on Google Cloud Platform. It is suitable for those with some prior experience in machine learning and data modeling.

Course was easy to follow.

"très bon cour pour initialiser avec Machine Learning "

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 Serverless Machine Learning with Tensorflow on Google Cloud Platform en Français with these activities:

Create a sample TensorFlow model

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Develop a better understanding of TensorFlow's functionalities.

Browse courses on Machine Learning Models

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Explore TensorFlow's documentation for model creation.
Implement a simple model using TensorFlow's API.

Solve practice problems on machine learning algorithms

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Strengthen understanding of machine learning algorithms and their application.

Browse courses on Machine Learning Algorithms

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Attempt practice problems from online resources or textbooks.
Discuss solutions with peers or TAs.

Participate in study groups or discussions with peers

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Gain diverse perspectives and clarify concepts through peer interaction.

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Join or create study groups.
Actively participate in discussions and ask questions.

Four other activities

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Create a visual representation of a machine learning model

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Enhance comprehension of model structure and functionality.

Browse courses on Data Visualization

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Choose a model and gather relevant data.
Use visualization tools to create diagrams or charts.

Follow tutorials on advanced machine learning techniques

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Gain exposure to cutting-edge machine learning techniques.

Browse courses on Advanced Machine Learning

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Identify reputable tutorials from online platforms or research papers.
Follow the tutorials step-by-step and implement the techniques.

Contribute to open-source machine learning projects

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Gain practical experience and connect with the machine learning community.

Browse courses on GitHub

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Identify open-source machine learning projects on platforms like GitHub.
Contribute by fixing bugs, adding features, or improving documentation.

Develop a machine learning application

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Apply machine learning concepts to solve real-world problems.

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Define the project scope and objectives.
Collect and preprocess data.
Train and evaluate machine learning models.
Deploy the application.

Career center

Learners who complete Serverless Machine Learning with Tensorflow on Google Cloud Platform en Français will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist designs, deploys, and optimizes machine learning models, which are valuable to businesses in a wide range of industries. This course introduces the fundamentals of machine learning and TensorFlow and can provide you with the foundational skills to develop applications as a Data Scientist. The hands-on nature of the course, reliant on demonstrations and practical exercises, will be particularly beneficial for your development.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers can apply a business understanding to complex problems and find technical solutions to those problems using machine learning. This course introduces the fundamentals of machine learning, TensorFlow, and Cloud ML, and it will equip you with the tools to help businesses improve their operations using machine learning.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers research, design, build, test, and maintain software. This course provides a foundation for Software Engineers who wish to use machine learning in their work. It will help Software Engineers build a foundation in machine learning and TensorFlow and will teach how to build, evaluate, and deploy machine learning models.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Quantitative Analysts (Quants) are specialists who use mathematical and statistical modeling to analyze and solve complex financial problems. This course provides a foundation in machine learning that can benefit Quants in their work. The course will introduce the fundamentals of TensorFlow and Cloud ML and will equip students with the tools to help businesses improve their operations using machine learning.

See salaries and explore the career path for Quantitative Analyst

Data Analyst

Data Analysts collect, analyze, interpret, and present data to help businesses make informed decisions. This course is a valuable introduction to machine learning for Data Analysts. It will help Data Analysts build a foundation in machine learning and TensorFlow and will teach how to build, evaluate, and deploy machine learning models.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts work with stakeholders to define business needs and develop solutions to meet those needs. This course may be useful for Business Analysts who want to develop a foundation in machine learning. It will provide a good overview of the field and will help Business Analysts understand how machine learning can be used to improve business processes.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers oversee the development of new products or features and work with engineers, designers, and other stakeholders to bring their vision to life. This course will be useful for Product Managers who want to develop a foundation in machine learning. It will provide a good overview of the field and will help Product Managers understand how machine learning can be used to improve products and features.

See salaries and explore the career path for Product Manager

Cloud Architect

Cloud Architects design and manage cloud computing systems. This course may be useful for Cloud Architects who want to develop a foundation in machine learning. It will provide a good overview of the field and will help Cloud Architects understand how machine learning can be used to improve cloud computing systems.

See salaries and explore the career path for Cloud Architect

DevOps Engineer

DevOps Engineers work to bridge the gap between development and operations. They are responsible for building, deploying, and maintaining software systems. This course may be useful for DevOps Engineers who want to develop a foundation in machine learning. It will provide a good overview of the field and will help DevOps Engineers understand how machine learning can be used to improve software development and deployment.

See salaries and explore the career path for DevOps Engineer

Data Engineer

Data Engineers design, build, and maintain data pipelines. This course may be useful for Data Engineers who want to develop a foundation in machine learning. It will provide a good overview of the field and will help Data Engineers understand how machine learning can be used to improve data pipelines.

See salaries and explore the career path for Data Engineer

Machine Learning Scientist

Machine Learning Scientists research and develop new machine learning algorithms and models. This course may be useful for Machine Learning Scientists who want to develop a foundation in TensorFlow and Cloud ML. It will provide a good overview of the field and will help Machine Learning Scientists understand how to build, evaluate, and deploy machine learning models.

See salaries and explore the career path for Machine Learning Scientist

Software Architect

Software Architects design and develop software systems. This course may be useful for Software Architects who want to develop a foundation in machine learning. It will provide a good overview of the field and will help Software Architects understand how machine learning can be used to improve software systems.

See salaries and explore the career path for Software Architect

Technical Program Manager

Technical Program Managers oversee the development of new products or features and work with engineers, designers, and other stakeholders to bring their vision to life. This course may be useful for Technical Program Managers who want to develop a foundation in machine learning. It will provide a good overview of the field and will help Technical Program Managers understand how machine learning can be used to improve products and features.

See salaries and explore the career path for Technical Program Manager

Product Designer

Product Designers work with stakeholders to define business needs and develop solutions to meet those needs. This course may be useful for Product Designers who want to develop a foundation in machine learning. It will provide a good overview of the field and will help Product Designers understand how machine learning can be used to improve products and features.

See salaries and explore the career path for Product Designer

Data Visualization Analyst

Data Visualization Analysts collect, analyze, interpret, and present data to help businesses make informed decisions. This course may be useful for Data Visualization Analysts who want to develop a foundation in machine learning. It will provide a good overview of the field and will help Data Visualization Analysts understand how machine learning can be used to improve data visualization.

See salaries and explore the career path for Data Visualization Analyst

Serverless Machine Learning with Tensorflow on Google Cloud Platform en Français

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Ml with tensorflow on google cloud

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