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Hernán Daniel Merlino
Este curso te brindará los conocimientos necesarios para la implementación de algoritmos de NLP. Mediante el uso de los últimos algoritmos más populares en NLP se procederá a dar solución a un conjunto de problemas propios del área.
Para realizar este curso es necesario contar con conocimientos de programación de nivel básico a medio, deseablemente conocimiento básico del lenguaje Python y es recomendable conocer los Jupyter Notebooks en el entorno Anaconda.
Read more
Este curso te brindará los conocimientos necesarios para la implementación de algoritmos de NLP. Mediante el uso de los últimos algoritmos más populares en NLP se procederá a dar solución a un conjunto de problemas propios del área.
Para realizar este curso es necesario contar con conocimientos de programación de nivel básico a medio, deseablemente conocimiento básico del lenguaje Python y es recomendable conocer los Jupyter Notebooks en el entorno Anaconda.
Read more
Este curso te brindará los conocimientos necesarios para la implementación de algoritmos de NLP. Mediante el uso de los últimos algoritmos más populares en NLP se procederá a dar solución a un conjunto de problemas propios del área.
Para realizar este curso es necesario contar con conocimientos de programación de nivel básico a medio, deseablemente conocimiento básico del lenguaje Python y es recomendable conocer los Jupyter Notebooks en el entorno Anaconda.
Para desarrollar aplicaciones se va a utilizar Python 3.6 o superior. Alternativamente se puede utilizar el entorno de Anaconda con la misma versión de Python.
Como editor de código, los ejemplos van a ser editados en el Notebook de Anaconda, pero el alumno puede utilizar cualquier editor de texto que reconozca notebooks de Anaconda.
Librerías que es necesario tener instaladas para realizar el curso: NLTK, Scikit-learn, Spacy y TensorFlow.
Register for this course and see more details by visiting:
OpenCourser.com/course/9zh5as/nlp
What's inside
Syllabus
Arquitectura y ciclo de vida de proyectos de NLP
Este módulo te permitirá obtener los conocimientos necesarios para poder definir modelos arquitectónicos de sistemas basados en NLP y su pasaje a producción.
Read more
Syllabus
Arquitectura y ciclo de vida de proyectos de NLP
Este módulo te permitirá obtener los conocimientos necesarios para poder definir modelos arquitectónicos de sistemas basados en NLP y su pasaje a producción.
Read more
MLOps
En este módulo se detalla el ciclo completo de las aplicaciones de NLP, pasaje a producción, mantenimiento y retroalimentación para la mejora perfectiva de las aplicaciones.
Últimos avances en NLP
En este módulo se presentará un conjunto de algoritmos que están corriendo los límites del NLP y potenciales áreas de aplicación.
Conclusión del curso
Repaso general de lo visto a lo largo de los distintos cursos, visión general de los proyectos de NLP y cambio de paradigma en la ingeniería de software.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Enseña habilidades y conocimientos muy relevantes para la industria
Desarrolla habilidades de nivel básico a medio, lo que lo hace accesible para una amplia gama de estudiantes
No requiere conocimientos previos en procesamiento de lenguaje natural
Cubre los últimos avances en el campo del procesamiento del lenguaje natural
Provee una base sólida para los estudiantes que buscan desarrollar habilidades en procesamiento del lenguaje natural
Enseña los últimos algoritmos y técnicas utilizados en el campo
Save this course
Save NLP System Architecture and Dev-Ops 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 NLP System Architecture and Dev-Ops with these
activities:
Revise basic programming principles
Show steps
Reinforces programming skills that will be essential for projects in this course.
Browse courses on
Python
Show steps
-
Review tutorials on data types, loops, conditionals, and functions
-
Complete practice exercises on coding platforms
Attend an NLP conference or meetup
Show steps
Connects with experts and peers to broaden perspectives on NLP.
Show steps
-
Research and identify upcoming NLP events
-
Register and attend the event
-
Network with other attendees and speakers
Solve NLP coding challenges
Show steps
Develops practical problem-solving skills in NLP.
Show steps
-
Find and register for online coding challenges (e.g., Kaggle, LeetCode)
-
Select challenges related to NLP concepts
Four other activities
Expand to see all activities and additional details
Show all seven activities
Help a beginner with their NLP journey
Show steps
Reinforces knowledge while assisting others in understanding NLP.
Browse courses on
Mentoring
Show steps
-
Identify a beginner who needs guidance
-
Provide support and answer their questions
-
Share resources and tips
Explore advanced NLP algorithms and frameworks
Show steps
Expands knowledge of NLP techniques covered in the course.
Show steps
-
Identify and select relevant tutorials from online resources
-
Follow the tutorials to implement and experiment with the algorithms
-
Document the learning and insights gained
Develop a text classification model
Show steps
Applies the course concepts to a real-world modeling task.
Browse courses on
Machine Learning
Show steps
-
Choose a relevant dataset
-
Clean and prepare the data
-
Train and evaluate different text classification models
-
Deploy the best model
Contribute to an NLP open-source project
Show steps
Involves in the NLP community and gains practical experience.
Browse courses on
Open Source
Show steps
-
Identify an NLP open-source project that aligns with interest
-
Review the project's documentation and codebase
-
Make meaningful contributions to the project
Revise basic programming principles
Show steps
Reinforces programming skills that will be essential for projects in this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review tutorials on data types, loops, conditionals, and functions
- Complete practice exercises on coding platforms
Attend an NLP conference or meetup
Show steps
Connects with experts and peers to broaden perspectives on NLP.
Show steps
Show steps
Show steps
- Research and identify upcoming NLP events
- Register and attend the event
- Network with other attendees and speakers
Solve NLP coding challenges
Show steps
Develops practical problem-solving skills in NLP.
Show steps
Show steps
Show steps
- Find and register for online coding challenges (e.g., Kaggle, LeetCode)
- Select challenges related to NLP concepts
Four other activities
Expand to see all activities and additional details
Show all seven activities
Help a beginner with their NLP journey
Show steps
Reinforces knowledge while assisting others in understanding NLP.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify a beginner who needs guidance
- Provide support and answer their questions
- Share resources and tips
Explore advanced NLP algorithms and frameworks
Show steps
Expands knowledge of NLP techniques covered in the course.
Show steps
Show steps
Show steps
- Identify and select relevant tutorials from online resources
- Follow the tutorials to implement and experiment with the algorithms
- Document the learning and insights gained
Develop a text classification model
Show steps
Applies the course concepts to a real-world modeling task.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a relevant dataset
- Clean and prepare the data
- Train and evaluate different text classification models
- Deploy the best model
Contribute to an NLP open-source project
Show steps
Involves in the NLP community and gains practical experience.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an NLP open-source project that aligns with interest
- Review the project's documentation and codebase
- Make meaningful contributions to the project
Career center
Learners who complete NLP System Architecture and Dev-Ops will develop knowledge and skills
that may be useful to these careers:
NLP Architect
NLP Architect
NLP Architects design and implement NLP systems. They work with engineers and data scientists to develop systems that can understand and generate human language. The NLP System Architecture and Dev-Ops course may be particularly useful for those looking to advance in this field, as it covers advanced NLP algorithms, architectural design, and lifecycle management of these systems.
NLP Researcher
NLP Researcher
NLP Researchers develop new NLP algorithms and techniques. They work in academia and industry to advance the state-of-the-art in NLP. The NLP System Architecture and Dev-Ops course may be particularly useful for those looking to advance in this field, as it covers advanced NLP algorithms, architectural design, and lifecycle management of these systems.
Computational Linguist
Computational Linguist
Computational Linguists use their expertise in linguistics and computer science to develop NLP models. They work on a variety of tasks, such as machine translation, speech recognition, and text summarization. The NLP System Architecture and Dev-Ops course may be useful for those seeking to advance in this field, as it covers advanced NLP algorithms, architectural design, and lifecycle management of these systems.
AI Engineer
AI Engineer
AI Engineers design, develop, and deploy AI systems. They work on a variety of tasks, such as machine learning, computer vision, and natural language processing. The NLP System Architecture and Dev-Ops course may be particularly useful for those looking to advance in the NLP subfield of AI, given its coverage of advanced NLP algorithms, architectural design, and lifecycle management of these systems.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers are responsible for the design and development of NLP models. They use their expertise in NLP algorithms, machine learning, and linguistics to build models that can understand and generate human language. The NLP System Architecture and Dev-Ops course may be particularly useful for those looking to advance their career, given its coverage of advanced NLP algorithms, architectural design, and lifecycle management of these systems.
DevOps Engineer
DevOps Engineer
DevOps Engineers are responsible for the deployment and maintenance of software systems. They work with developers and operations teams to ensure that systems are running smoothly. Those working with NLP systems may find that the NLP System Architecture and Dev-Ops course may be useful, as it covers the fundamentals of NLP algorithm development and implementation.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers apply the principles of machine learning to develop new models and improve existing ones. They research, design, implement and evaluate machine learning models to solve real-world problems. Those working on Natural Language Processing (NLP) models would find that the NLP System Architecture and Dev-Ops course may be useful, particularly with the topics covering the lifecycle management, deployment, and evaluation of these NLP models.
Software Engineer
Software Engineer
Software Engineers apply the principles of software engineering to the design, development, and maintenance of software systems. They research, design, implement and evaluate these systems to solve real-world problems. Software Engineers looking to advance their skills in developing and deploying NLP models would find that the NLP System Architecture and DevOps course may be useful.
Data Scientist
Data Scientist
Data Scientists work in various industries and solve problems through the use of data. Whether it's to improve product offerings or streamline business processes, their expertise in data modeling, machine learning, artificial intelligence, and statistics aids in their discoveries. Those tasked with NLP-related projects often build and train Natural Language Processing (NLP) models, evaluate performance, and deploy these models in order to solve problems. Due to the nature of their work, Data Scientists would find that the NLP System Architecture and Dev-Ops course may be useful given that it'll teach the fundamentals in the development and implementation of NLP algorithms in addition to the lifecycle management of these models.
User Experience Designer
User Experience Designer
User Experience Designers are responsible for the design and development of user interfaces. They use their expertise in human-computer interaction to create interfaces that are easy to use and understand. Those working on NLP-based interfaces may find that the NLP System Architecture and Dev-Ops course may be useful, as it covers the fundamentals of NLP algorithm development and implementation.
Speech Scientist
Speech Scientist
Speech Scientists use their expertise in speech and hearing science to develop NLP models. They work on a variety of tasks, such as speech recognition, speech synthesis, and speaker recognition. Those working with NLP may find that the NLP System Architecture and Dev-Ops course may be useful, as it covers the fundamentals of NLP algorithm development and implementation, which can enhance their ability to develop more robust speech models.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to help organizations make better decisions. They collect, clean, analyze, and interpret data to identify trends and patterns. Those working in NLP may be tasked with building models to extract meaningful insights from text data. The NLP System Architecture and Dev-Ops course may be useful due to its coverage of NLP algorithm development and implementation.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines. They work with data scientists and engineers to ensure that data is available and accessible for analysis. Those working with NLP data may find that the NLP System Architecture and Dev-Ops course may be useful, as it covers the fundamentals of NLP algorithm development and implementation.
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 new products to market. Those working with NLP-based products may find that the NLP System Architecture and Dev-Ops course may be useful, as it covers the fundamentals of NLP algorithm development and implementation.
Data Analyst
Data Analyst
Data Analysts collect, clean, analyze, and interpret data to help organizations make informed decisions. They use their skills in statistics, machine learning, and data visualization to extract meaningful insights from data. Data Analysts who wish to specialize in NLP may find that the NLP System Architecture and Dev-Ops course may be useful, as it covers the fundamentals of NLP algorithm development and implementation.
For more career information including salaries, visit:
OpenCourser.com/course/9zh5as/nlp
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
NLP System Architecture and Dev-Ops.
This comprehensive textbook provides a thorough foundation in the fundamentals of speech and language processing. While it may not focus specifically on NLP applications, it offers valuable insights into the underlying concepts and techniques used in NLP systems.
This practical guide focuses on building NLP models using TensorFlow 2, a widely adopted framework for deep learning. It aligns well with the course's emphasis on TensorFlow and provides hands-on examples of implementing various NLP tasks, such as text classification, named entity recognition, and machine translation.
Provides a comprehensive overview of deep learning for natural language processing, covering topics such as neural networks, recurrent neural networks, and convolutional neural networks. It also covers more advanced topics such as attention mechanisms and transformer networks.
This comprehensive guide to deep learning with Python serves as an excellent resource for delving deeper into the technical aspects of deep learning models used in NLP. The book provides a thorough explanation of neural networks, convolutional neural networks, recurrent neural networks, and other advanced techniques commonly employed in modern NLP systems.
Save
Provides a comprehensive overview of natural language processing with TensorFlow, covering topics such as text preprocessing, tokenization, stemming, and lemmatization. It also covers more advanced topics such as machine learning for NLP and deep learning for NLP.
Provides a comprehensive overview of natural language processing with PyTorch, covering topics such as text preprocessing, tokenization, stemming, and lemmatization. It also covers more advanced topics such as machine learning for NLP and deep learning for NLP.
Provides a comprehensive overview of natural language processing, covering topics such as text preprocessing, tokenization, stemming, and lemmatization. It also covers more advanced topics such as machine learning for NLP and deep learning for NLP.
This practical guide offers a hands-on approach to NLP, focusing on building end-to-end NLP pipelines. While it may not cover the latest advances in NLP, it provides a solid foundation in the core principles and techniques used in NLP systems.
Esta guía práctica se centra en el uso de la biblioteca NLTK para tareas de PNL, lo que permite a los lectores explorar las capacidades de la biblioteca.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9zh5as/nlp
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Level
Introductory
Via
Coursera
Institution
Universidad Austral
Instructor
Hernán Daniel Merlino
Language
Español
This course belongs to a
collection
called
Procesamiento de Lenguaje Natural. It's comprised of four courses:
- NLP Modelos y Algoritmos
- Introducción al procesamiento de lenguaje natural
- Limpieza de datos para el procesamiento de lenguaje natural
- NLP System Architecture and Dev-Ops (viewing)
Good to know
Enseña habilidades y conocimientos muy relevantes para la industria
Desarrolla habilidades de nivel básico a medio, lo que lo hace accesible para una amplia gama de estudiantes
No requiere conocimientos previos en procesamiento de lenguaje natural
Cubre los últimos avances en el campo del procesamiento del lenguaje natural
Provee una base sólida para los estudiantes que buscan desarrollar habilidades en procesamiento del lenguaje natural
Enseña los últimos algoritmos y técnicas utilizados en el campo
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