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ELINGUI Pascal Uriel
Dans ce projet guidé, vous créerez un modèle de Machine Learning d’analyse de sentiments par classification de textes avec Tensorflow, en utilisant le plongement de mots (Word Embedding). Vous allez vous exercer avec des données collectées sur le site www.allocine.fr
Le word embedding est une méthode d'apprentissage d'une représentation de mots utilisée traitement automatique des langues. Il donne d’excellents résultats comme vous pourrez le constater dans ce projet guidé.
Read more
Dans ce projet guidé, vous créerez un modèle de Machine Learning d’analyse de sentiments par classification de textes avec Tensorflow, en utilisant le plongement de mots (Word Embedding). Vous allez vous exercer avec des données collectées sur le site www.allocine.fr
Le word embedding est une méthode d'apprentissage d'une représentation de mots utilisée traitement automatique des langues. Il donne d’excellents résultats comme vous pourrez le constater dans ce projet guidé.
Read more
Dans ce projet guidé, vous créerez un modèle de Machine Learning d’analyse de sentiments par classification de textes avec Tensorflow, en utilisant le plongement de mots (Word Embedding). Vous allez vous exercer avec des données collectées sur le site www.allocine.fr
Le word embedding est une méthode d'apprentissage d'une représentation de mots utilisée traitement automatique des langues. Il donne d’excellents résultats comme vous pourrez le constater dans ce projet guidé.
Ce cours est destiné aux ingénieurs en Machine Learning, au Data Scientists et tous les curieux désireux d’apprendre à faire de la classification de textes facilement.
Register for this course and see more details by visiting:
OpenCourser.com/course/6nbsfy/tensorflow
What's inside
Syllabus
Project Overview
Dans ce projet guidé, vous créerez un modèle de Machine Learning d’analyse de sentiments par classification de textes avec Tensorflow, en utilisant le plongement de mots (Word Embedding). Vous allez vous exercer avec des données collectées sur le site www.allocine.fr
Le word embedding est une méthode d'apprentissage d'une représentation de mots utilisée traitement automatique des langues. Il donne d’excellents résultats comme vous pourrez le constater dans ce projet guidé.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Le cours utilise Tensorflow, une bibliothèque largement utilisée dans l'industrie pour le traitement de données et l'apprentissage automatique
Le cours aborde le plongement de mots, une technique de représentation de texte essentielle au sein du traitement automatique des langues
Le cours se concentre sur l'analyse des sentiments, une compétence clé en intelligence artificielle et en sciences des données
Les données du projet proviennent d'Allociné, une source fiable et pertinente pour l'analyse des sentiments dans le domaine du cinéma
Le cours est conçu pour les ingénieurs en apprentissage automatique, les scientifiques des données, ainsi que les personnes intéressées par la classification de textes
Le cours est dispensé par Pascal Uriel, un expert en apprentissage automatique reconnu
Save this course
Save Tensorflow : Analyse de Sentiments avec Word Embedding to your list so you can find it easily later:
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Reviews summary
French sentiment analysis
This course will introduce you to natural language processing in French using Tensorflow with word embedding to analyze sentiment. Students report that the instructor is clear and that the course is refreshing. Some students complain about the cost compared to the value.
Course is in French.
"Great course. It is refreshing to taking in French."
Instructor provides clear instruction.
"The instructor is very clear!"
Some students feel it is overpriced.
"I do not recommend taking this type of course, take one and pass it, however after a few days I have tried to review the material, and my surprise is that it asks me to pay again to be able to review the material."
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 Tensorflow : Analyse de Sentiments avec Word Embedding with these
activities:
Compile Course Resources and Materials
Show steps
Get started by compiling and reviewing all the course materials and resources (e.g., syllabus, readings, videos, assignments) to gain an overview of the course structure and key concepts.
Show steps
-
Review the course syllabus to understand the course structure and objectives.
-
Gather all required readings, videos, and other materials.
-
Organize the materials in a logical manner for easy access.
Follow Online Tutorials for Text Classification with Tensorflow
Show steps
Expand your knowledge and skills by following guided tutorials that provide step-by-step instructions on text classification with Tensorflow.
Show steps
-
Find online tutorials from reputable sources that cover text classification with Tensorflow.
-
Follow the tutorials carefully, replicating the code and experimenting with different parameters.
-
Troubleshoot any errors or issues encountered during the tutorial.
Practice Text Classification with Tensorflow
Show steps
Solidify your understanding of Tensorflow and text classification by working through practice drills and exercises.
Browse courses on
TensorFlow
Show steps
-
Find online tutorials or resources that provide practice exercises for text classification with Tensorflow.
-
Attempt the exercises and debug any errors you encounter.
-
Review the solutions and identify areas where you can improve.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Compile a Toolkit of Resources for Text Classification
Show steps
Organize and gather valuable resources related to text classification, including tools, libraries, datasets, and articles.
Show steps
-
Identify and collect relevant resources from the course materials and external sources.
-
Categorize and organize the resources for easy access and reference.
Create a Visual Summary of the Word Embedding Concept
Show steps
Enhance your understanding and retention of the word embedding concept by creating a visual representation, such as a diagram, infographic, or presentation.
Show steps
-
Gather information about word embedding from the course materials and external sources.
-
Brainstorm ideas for visually representing the concept.
-
Create a visual representation using appropriate tools (e.g., diagrams, charts, videos).
Develop a Machine Learning Model for Sentiment Analysis
Show steps
Apply your knowledge and skills by developing a machine learning model that can perform sentiment analysis on text data.
Browse courses on
Machine Learning Model
Show steps
-
Gather and prepare a dataset of text data with sentiment labels.
-
Choose and implement a machine learning algorithm for sentiment analysis.
-
Train and evaluate the model using the prepared dataset.
-
Deploy and test the model on new data to demonstrate its effectiveness.
Participate in Kaggle Competitions
Show steps
Challenge yourself and test your skills by participating in Kaggle competitions related to text classification or machine learning.
Browse courses on
Kaggle
Show steps
-
Identify relevant Kaggle competitions that align with the course topics.
-
Form a team or collaborate with others to participate in the competition.
-
Develop and implement solutions to the competition challenges.
Volunteer as a Mentor
Show steps
Enhance your understanding and solidify your knowledge by mentoring others in the same course or related field.
Show steps
-
Identify opportunities to mentor other students or colleagues.
-
Provide guidance, support, and advice to mentees.
Compile Course Resources and Materials
Show steps
Get started by compiling and reviewing all the course materials and resources (e.g., syllabus, readings, videos, assignments) to gain an overview of the course structure and key concepts.
Show steps
Show steps
Show steps
- Review the course syllabus to understand the course structure and objectives.
- Gather all required readings, videos, and other materials.
- Organize the materials in a logical manner for easy access.
Follow Online Tutorials for Text Classification with Tensorflow
Show steps
Expand your knowledge and skills by following guided tutorials that provide step-by-step instructions on text classification with Tensorflow.
Show steps
Show steps
Show steps
- Find online tutorials from reputable sources that cover text classification with Tensorflow.
- Follow the tutorials carefully, replicating the code and experimenting with different parameters.
- Troubleshoot any errors or issues encountered during the tutorial.
Practice Text Classification with Tensorflow
Show steps
Solidify your understanding of Tensorflow and text classification by working through practice drills and exercises.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find online tutorials or resources that provide practice exercises for text classification with Tensorflow.
- Attempt the exercises and debug any errors you encounter.
- Review the solutions and identify areas where you can improve.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Compile a Toolkit of Resources for Text Classification
Show steps
Organize and gather valuable resources related to text classification, including tools, libraries, datasets, and articles.
Show steps
Show steps
Show steps
- Identify and collect relevant resources from the course materials and external sources.
- Categorize and organize the resources for easy access and reference.
Create a Visual Summary of the Word Embedding Concept
Show steps
Enhance your understanding and retention of the word embedding concept by creating a visual representation, such as a diagram, infographic, or presentation.
Show steps
Show steps
Show steps
- Gather information about word embedding from the course materials and external sources.
- Brainstorm ideas for visually representing the concept.
- Create a visual representation using appropriate tools (e.g., diagrams, charts, videos).
Develop a Machine Learning Model for Sentiment Analysis
Show steps
Apply your knowledge and skills by developing a machine learning model that can perform sentiment analysis on text data.
Browse courses on
Machine Learning Model
Show steps
Show steps
Show steps
- Gather and prepare a dataset of text data with sentiment labels.
- Choose and implement a machine learning algorithm for sentiment analysis.
- Train and evaluate the model using the prepared dataset.
- Deploy and test the model on new data to demonstrate its effectiveness.
Participate in Kaggle Competitions
Show steps
Challenge yourself and test your skills by participating in Kaggle competitions related to text classification or machine learning.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Identify relevant Kaggle competitions that align with the course topics.
- Form a team or collaborate with others to participate in the competition.
- Develop and implement solutions to the competition challenges.
Volunteer as a Mentor
Show steps
Enhance your understanding and solidify your knowledge by mentoring others in the same course or related field.
Show steps
Show steps
Show steps
- Identify opportunities to mentor other students or colleagues.
- Provide guidance, support, and advice to mentees.
Career center
Learners who complete Tensorflow : Analyse de Sentiments avec Word Embedding will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design and develop systems that can understand and generate human language. This course can help you get started in this field by providing you with a foundation in natural language processing and word embedding techniques.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain the machine learning models that power a wide range of applications, from self-driving cars to fraud detection systems. This course can help you get started in this field by providing you with a foundation in natural language processing and word embedding techniques.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. This course can help you develop the skills you need to succeed in this role by providing you with a foundation in natural language processing, word embedding techniques, and machine learning modeling.
Text Analyst
Text Analyst
Text Analysts use natural language processing to analyze text data. They identify patterns and trends in text, and they develop insights that can be used to improve business decisions. This course can help you develop the skills you need to succeed in this role by providing you with a foundation in natural language processing and word embedding techniques.
Editor
Editor
Editors review and edit written content, such as articles, books, and websites. They use natural language processing to identify and correct errors in grammar, spelling, and style. This course can help you develop the skills you need to succeed in this role by providing you with a foundation in natural language processing and word embedding techniques.
User Experience Researcher
User Experience Researcher
User Experience Researchers study how users interact with websites and other online platforms. They use natural language processing to analyze user feedback, and they develop insights that can be used to improve the user experience. This course can help you build a foundation in natural language processing and word embedding techniques.
Computational Linguist
Computational Linguist
Computational Linguists use computers to study human language. They develop new methods for analyzing and understanding language, and they create new applications that use natural language processing. This course can help you build a foundation in natural language processing and word embedding techniques.
Social Media Manager
Social Media Manager
Social Media Managers create and manage social media content for businesses and organizations. They use natural language processing to identify and target relevant audiences, and they develop strategies for engaging with customers. This course can help you develop the skills you need to succeed in this role by providing you with a foundation in natural language processing and word embedding techniques.
Technical Writer
Technical Writer
Technical Writers create and maintain documentation for software and other technical products. They use natural language processing to ensure that documentation is clear and concise. This course can help you build a foundation in natural language processing and word embedding techniques.
Customer Success Manager
Customer Success Manager
Customer Success Managers help businesses retain and grow their customer base. They use natural language processing to analyze customer feedback, identify areas for improvement, and develop strategies for increasing customer satisfaction. This course can help you build a foundation in natural language processing and word embedding techniques.
Salesperson
Salesperson
Salespeople sell products and services to customers. They use natural language processing to identify and target potential customers, and they develop strategies for closing deals. This course can help you build a foundation in natural language processing and word embedding techniques.
Community Manager
Community Manager
Community Managers build and manage online communities for businesses and organizations. They use natural language processing to engage with members, answer questions, and resolve conflicts. This course can help you build a foundation in natural language processing and word embedding techniques.
Search Engine Optimizer
Search Engine Optimizer
Search Engine Optimizers help businesses improve their visibility in search engine results pages. They use natural language processing to identify and target relevant keywords, and they develop strategies for optimizing website content and structure. This course can help you develop the skills you need to succeed in this role by providing you with a foundation in natural language processing and word embedding techniques.
Content Curator
Content Curator
Content Curators find and organize content for websites and other online platforms. They use natural language processing to identify and select relevant content, and they develop strategies for promoting and distributing content. This course can help you get started in this field by providing you with a foundation in natural language processing and word embedding techniques.
Marketing Manager
Marketing Manager
Marketing managers develop and execute marketing campaigns for businesses and organizations. They use natural language processing to identify and target relevant audiences, and they develop strategies for reaching those audiences. This course can help you build a foundation in natural language processing and word embedding techniques.
For more career information including salaries, visit:
OpenCourser.com/course/6nbsfy/tensorflow
Reading list
We've selected 12 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
Tensorflow : Analyse de Sentiments avec Word Embedding.
Provides a comprehensive overview of natural language processing (NLP) techniques, including word embedding. It would be a valuable resource for students who want to learn more about NLP and its applications.
Provides a practical introduction to deep learning, a powerful machine learning technique that is used in a variety of applications, including natural language processing. It would be a valuable resource for students who want to learn more about deep learning and its applications.
Provides a practical introduction to TensorFlow, a popular open-source machine learning library. It would be a valuable resource for students who want to learn more about TensorFlow and its applications.
Provides a comprehensive overview of NLP techniques, including word embedding. It would be a valuable resource for students who want to learn more about NLP and its applications.
Provides a comprehensive overview of deep learning techniques for NLP. It would be a valuable resource for students who want to learn more about deep learning and its applications in NLP.
Provides a comprehensive overview of machine learning, including supervised learning, unsupervised learning, and deep learning. It would be a valuable resource for students who want to learn more about machine learning and its applications.
Provides a practical introduction to NLP with TensorFlow. It would be a valuable resource for students who want to learn more about NLP and its applications in TensorFlow.
Provides a comprehensive overview of word embedding techniques. It would be a valuable resource for students who want to learn more about word embedding and its applications.
Provides a comprehensive overview of sentiment analysis techniques. It would be a valuable resource for students who want to learn more about sentiment analysis and its applications.
Save
Provides a comprehensive overview of machine learning, including supervised learning, unsupervised learning, and deep learning. It would be a valuable resource for students who want to learn more about machine learning and its applications.
Save
Provides a comprehensive overview of deep learning, a powerful machine learning technique that is used in a variety of applications, including natural language processing. It would be a valuable resource for students who want to learn more about deep learning and its applications.
Provides a comprehensive overview of NLP, including word embedding. It would be a valuable resource for students who want to learn more about NLP and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6nbsfy/tensorflow
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
ELINGUI Pascal Uriel
Language
Français
Good to know
Le cours utilise Tensorflow, une bibliothèque largement utilisée dans l'industrie pour le traitement de données et l'apprentissage automatique
Le cours aborde le plongement de mots, une technique de représentation de texte essentielle au sein du traitement automatique des langues
Le cours se concentre sur l'analyse des sentiments, une compétence clé en intelligence artificielle et en sciences des données
Les données du projet proviennent d'Allociné, une source fiable et pertinente pour l'analyse des sentiments dans le domaine du cinéma
Le cours est conçu pour les ingénieurs en apprentissage automatique, les scientifiques des données, ainsi que les personnes intéressées par la classification de textes
Le cours est dispensé par Pascal Uriel, un expert en apprentissage automatique reconnu
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