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Au début de ce cours, vous trouverez une discussion concernant les données, expliquant comment améliorer leur qualité et comment effectuer des analyses exploratoires. Ensuite, nous vous présenterons Vertex AI AutoML et vous expliquerons comment créer, entraîner et déployer un modèle de machine learning (ML) sans écrire une ligne de code. Vous découvrirez également les avantages de BigQuery ML. Enfin, nous verrons comment optimiser un modèle de ML, et en quoi la généralisation ainsi que l'échantillonnage peuvent vous aider à évaluer la qualité des modèles de ML destinés à un entraînement personnalisé.
Register for this course and see more details by visiting:
OpenCourser.com/course/y2nzg6/launching
What's inside
Syllabus
Présentation
Ce module présente le cours et ses objectifs.
Familiarisation : effectuer une analyse exploratoire des données pour les améliorer
Dans ce module, nous verrons comment améliorer la qualité de nos données et effectuer une analyse exploratoire de celles-ci. Nous évoquerons également l'importance du nettoyage de données en machine learning et son impact sur la qualité. Sachez par exemple que des valeurs manquantes peuvent fausser les résultats. Enfin, vous découvrirez pourquoi il est primordial d'explorer vos données. Une fois l'ensemble de données nettoyé, vous effectuerez une analyse exploratoire de celui-ci.
Read more
Syllabus
Présentation
Ce module présente le cours et ses objectifs.
Familiarisation : effectuer une analyse exploratoire des données pour les améliorer
Dans ce module, nous verrons comment améliorer la qualité de nos données et effectuer une analyse exploratoire de celles-ci. Nous évoquerons également l'importance du nettoyage de données en machine learning et son impact sur la qualité. Sachez par exemple que des valeurs manquantes peuvent fausser les résultats. Enfin, vous découvrirez pourquoi il est primordial d'explorer vos données. Une fois l'ensemble de données nettoyé, vous effectuerez une analyse exploratoire de celui-ci.
Read more
Le machine learning en pratique
Dans ce module, nous vous présenterons quelques-uns des principaux types de machine learning pour que vous puissiez progresser plus rapidement en tant qu'utilisateur du ML.
Entraîner des modèles AutoML à l'aide de Vertex AI
Dans ce module, nous verrons comment entraîner des modèles AutoML à l'aide de Vertex AI.
BigQuery Machine Learning : développer des modèles de ML dans l'espace de stockage de vos données
Dans ce module, nous vous présenterons BigQuery ML et ses fonctionnalités.
Optimisation
Dans ce module, nous vous apprendrons à optimiser vos modèles de ML.
Généralisation et échantillonnage
Il est maintenant temps de répondre à une question plutôt bizarre : dans quels cas ne faut-il pas choisir le modèle de ML le plus précis ? Comme brièvement évoqué dans le module précédent sur l'optimisation, un modèle présentant une métrique de perte égale à zéro pour votre ensemble de données d'entraînement ne sera pas forcément efficace sur de nouvelles données réelles. Dans ce module, vous apprendrez à créer des ensembles de données d'entraînement, d'évaluation et de test reproductibles, ainsi qu'à définir des benchmarks de performances.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Cette formation s'adresse aux utilisateurs de ML qui cherchent à améliorer leurs compétences en matière d'analyse des données et de modélisation ML
Elle convient également aux professionnels de l'informatique et aux analystes de données qui souhaitent se familiariser avec les principes de la modélisation ML
Les débutants en ML peuvent également bénéficier de cette formation, car elle aborde les concepts de base d'une manière accessible
Le module sur BigQuery ML est particulièrement pertinent pour ceux qui travaillent avec de grands ensembles de données
La formation est dispensée par Google Cloud Training, qui est reconnu pour son expertise dans le domaine du ML
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Save Launching into Machine Learning en Français to your list so you can find it easily later:
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Reviews summary
Excellent french first course
This course offers a structured introduction to machine learning in French, with high marks for its presentation and ease of use. Many students have found this course helpful for developing a solid foundation in the principles of machine learning. The course is especially valued for its use of AutoML, which allows learners to develop machine learning models with minimal coding.
Great for those new to machine learning
"le cours est trés bon"
Lots of practical, hands-on examples
"Essentiel pour disposer des bases en Maching Learning."
Use simplified AutoML models
In-depth coverage of ML history
"Honestly the part about the history was incredible hard to follow."
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 Launching into Machine Learning en Français with these
activities:
Brush up on data cleaning techniques
Show steps
Refresh your understanding of data cleaning techniques to better prepare for the course content on data exploration and analysis.
Show steps
-
Review online tutorials on data cleaning
-
Practice data cleaning techniques using a sample dataset
Organize and review course notes and materials
Show steps
Enhance your understanding by organizing and reviewing course notes and materials regularly.
Show steps
-
Gather and organize lecture notes, readings, and assignments
-
Review the materials periodically to reinforce your understanding
-
Summarize key concepts and create study aids
Complete coding exercises on machine learning algorithms
Show steps
Reinforce your understanding of machine learning algorithms by completing coding exercises.
Browse courses on
Machine Learning Algorithms
Show steps
-
Identify a coding platform with machine learning exercises
-
Select exercises covering various machine learning algorithms
-
Solve the exercises and review your solutions
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a study session on machine learning optimization
Show steps
Engage with peers to discuss and practice machine learning optimization techniques.
Browse courses on
Machine Learning Optimization
Show steps
-
Find a study group or organize one with peers
-
Choose a topic related to machine learning optimization
-
Prepare discussion points and examples
-
Meet regularly to discuss and solve problems
Follow tutorials on BigQuery ML
Show steps
Deepen your knowledge of BigQuery ML by following guided tutorials.
Browse courses on
BigQuery ML
Show steps
-
Identify online tutorials on BigQuery ML
-
Follow the tutorials step-by-step
-
Experiment with BigQuery ML using the provided datasets
Practice solving problems on model generalization and overfitting
Show steps
Improve your understanding of model generalization and overfitting by solving practice problems.
Browse courses on
Generalization
Show steps
-
Identify resources with practice problems on model generalization and overfitting
-
Solve the problems and analyze your results
-
Research techniques for improving model generalization
Build a machine learning model using AutoML
Show steps
Gain hands-on experience by building a machine learning model using AutoML.
Browse courses on
AutoML
Show steps
-
Choose a dataset and define the problem statement
-
Train an AutoML model using Vertex AI
-
Evaluate the model's performance
-
Deploy the model and monitor its performance
Brush up on data cleaning techniques
Show steps
Refresh your understanding of data cleaning techniques to better prepare for the course content on data exploration and analysis.
Show steps
Show steps
Show steps
- Review online tutorials on data cleaning
- Practice data cleaning techniques using a sample dataset
Organize and review course notes and materials
Show steps
Enhance your understanding by organizing and reviewing course notes and materials regularly.
Show steps
Show steps
Show steps
- Gather and organize lecture notes, readings, and assignments
- Review the materials periodically to reinforce your understanding
- Summarize key concepts and create study aids
Complete coding exercises on machine learning algorithms
Show steps
Reinforce your understanding of machine learning algorithms by completing coding exercises.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Identify a coding platform with machine learning exercises
- Select exercises covering various machine learning algorithms
- Solve the exercises and review your solutions
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a study session on machine learning optimization
Show steps
Engage with peers to discuss and practice machine learning optimization techniques.
Browse courses on
Machine Learning Optimization
Show steps
Show steps
Show steps
- Find a study group or organize one with peers
- Choose a topic related to machine learning optimization
- Prepare discussion points and examples
- Meet regularly to discuss and solve problems
Follow tutorials on BigQuery ML
Show steps
Deepen your knowledge of BigQuery ML by following guided tutorials.
Browse courses on
BigQuery ML
Show steps
Show steps
Show steps
- Identify online tutorials on BigQuery ML
- Follow the tutorials step-by-step
- Experiment with BigQuery ML using the provided datasets
Practice solving problems on model generalization and overfitting
Show steps
Improve your understanding of model generalization and overfitting by solving practice problems.
Browse courses on
Generalization
Show steps
Show steps
Show steps
- Identify resources with practice problems on model generalization and overfitting
- Solve the problems and analyze your results
- Research techniques for improving model generalization
Build a machine learning model using AutoML
Show steps
Gain hands-on experience by building a machine learning model using AutoML.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Choose a dataset and define the problem statement
- Train an AutoML model using Vertex AI
- Evaluate the model's performance
- Deploy the model and monitor its performance
Career center
Learners who complete Launching into Machine Learning en Français will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They design and develop algorithms that can learn from data and make predictions. This course can help you build a foundation in machine learning, which is essential for success in this role. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, mathematics, and computer science to extract insights from data. They develop and implement machine learning models to solve business problems. This course can help you build a foundation in machine learning, which is essential for success in this role. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages and software development tools to create software that meets the needs of users. This course can help you build a foundation in machine learning, which is becoming increasingly important in software development. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and data to identify and solve business problems. They use data analysis techniques to understand the needs of customers and develop solutions to meet those needs. This course can help you build a foundation in machine learning, which is becoming increasingly important in business analysis. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to create products that meet the needs of customers. This course can help you build a foundation in machine learning, which is becoming increasingly important in product management. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Data Analyst
Data Analyst
Data Analysts use their knowledge of statistics, mathematics, and computer science to analyze data and extract insights. They use their findings to help businesses make better decisions. This course can help you build a foundation in machine learning, which is becoming increasingly important in data analysis. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, statistics, and computer science to develop and implement mathematical models for financial markets. They use these models to make investment decisions. This course can help you build a foundation in machine learning, which is becoming increasingly important in quantitative analysis. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Statistician
Statistician
Statisticians use their knowledge of mathematics, statistics, and computer science to collect, analyze, and interpret data. They use their findings to help businesses and organizations make better decisions. This course can help you build a foundation in machine learning, which is becoming increasingly important in statistics. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics, statistics, and computer science to develop and implement mathematical models for business and industrial problems. They use these models to improve efficiency and productivity. This course can help you build a foundation in machine learning, which is becoming increasingly important in operations research. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Market Researcher
Market Researcher
Market Researchers use their knowledge of statistics, mathematics, and computer science to collect, analyze, and interpret data about markets and consumers. They use their findings to help businesses make better decisions about product development, marketing, and sales. This course can help you build a foundation in machine learning, which is becoming increasingly important in market research. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of mathematics, statistics, and computer science to analyze financial data and make investment recommendations. They use their findings to help businesses and individuals make better financial decisions. This course may be useful to you if you are interested in a career as a financial analyst. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Actuary
Actuary
Actuaries use their knowledge of mathematics, statistics, and computer science to assess risk and make financial decisions. They use their findings to help businesses and individuals make better financial decisions. This course may be useful to you if you are interested in a career as an actuary. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Epidemiologist
Epidemiologist
Epidemiologists use their knowledge of mathematics, statistics, and computer science to study the causes of disease and injury. They use their findings to help public health officials develop and implement programs to prevent and control disease. This course may be useful to you if you are interested in a career as an epidemiologist. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Economist
Economist
Economists use their knowledge of mathematics, statistics, and computer science to study the economy and make policy recommendations. They use their findings to help governments and businesses make better economic decisions. This course may be useful to you if you are interested in a career as an economist. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Teacher
Teacher
Teachers use their knowledge of mathematics, statistics, and computer science to teach students about these subjects. They use their skills to help students develop critical thinking skills and problem-solving abilities. This course may be useful to you if you are interested in a career as a teacher. You will learn about data exploration, model training, and model evaluation. You will also learn about some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
For more career information including salaries, visit:
OpenCourser.com/course/y2nzg6/launching
Reading list
We've selected nine 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
Launching into Machine Learning en Français.
Save
Provides a comprehensive overview of deep learning. It good resource for students and researchers who want to learn about the latest advances in deep learning.
Provides a comprehensive overview of deep learning. It good resource for students and researchers who want to learn about the latest advances in deep learning.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It good resource for students and researchers who want to learn about the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of pattern recognition and machine learning. It good resource for students and researchers who want to learn about the theoretical foundations of machine learning.
Provides a comprehensive overview of statistical learning. It good resource for students and researchers who want to learn about the theoretical foundations of statistical learning.
Provides a comprehensive overview of machine learning. It good resource for students and researchers who want to learn about the theoretical foundations of machine learning.
Provides a practical introduction to machine learning using Python. It good resource for beginners who want to learn how to use machine learning libraries to build and train models.
Provides a practical introduction to machine learning using Python. It good resource for beginners who want to learn how to use machine learning libraries to build and train models.
Provides a practical introduction to machine learning for programmers. It good resource for programmers who want to learn how to use machine learning to solve real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y2nzg6/launching
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Effort
5 à 7 heures par semaine
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Français
Transcript
English
This course belongs to a
collection
called
Machine Learning with TensorFlow on Google Cloud en Français. It's comprised of five courses:
- How Google does Machine Learning en Français
- Launching into Machine Learning en Français (viewing)
- Feature Engineering en Français
- Intro to TensorFlow en Français
- Art and Science of Machine Learning en Français
Good to know
Cette formation s'adresse aux utilisateurs de ML qui cherchent à améliorer leurs compétences en matière d'analyse des données et de modélisation ML
Elle convient également aux professionnels de l'informatique et aux analystes de données qui souhaitent se familiariser avec les principes de la modélisation ML
Les débutants en ML peuvent également bénéficier de cette formation, car elle aborde les concepts de base d'une manière accessible
Le module sur BigQuery ML est particulièrement pertinent pour ceux qui travaillent avec de grands ensembles de données
La formation est dispensée par Google Cloud Training, qui est reconnu pour son expertise dans le domaine du ML
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