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Creating Named Entity Recognition Systems with Python
Andrei Pruteanu
Andrei Pruteanu
This course shows how data professionals and software developers make use of the Python language in order to create Named Entity Recognition (NER) systems by leveraging the language’s powerful set of open-source NLP libraries.
Read more
This course shows how data professionals and software developers make use of the Python language in order to create Named Entity Recognition (NER) systems by leveraging the language’s powerful set of open-source NLP libraries.
Read more
This course shows how data professionals and software developers make use of the Python language in order to create Named Entity Recognition (NER) systems by leveraging the language’s powerful set of open-source NLP libraries.
In this course, Creating Named Entity Recognition Systems with Python, you'll look at how data professionals and software developers make use of the Python language.
First, you'll explore the unique ability of such systems to perform information retrieval by identifying specific classes of entities in texts.
Next, you'll learn how to install prerequisite tools and how to create in a step-by-step manner all the specific components of performant NER systems. Finally, you'll be able to create Named Entity Recognition (NER) systems by leveraging the language’s powerful set of open-source NLP libraries. When you’re finished with this course, you’ll have the skills and knowledge of creating named entity recognition systems with Python
Register for this course and see more details by visiting:
OpenCourser.com/course/56pj6h/creating
What's inside
Syllabus
Course Overview
Getting Started
Preprocessing Data for NER Training
Building Linear Classifiers for NER Systems
Read more
Syllabus
Course Overview
Getting Started
Preprocessing Data for NER Training
Building Linear Classifiers for NER Systems
Read more
Building Conditional Random Fields (CRFs)
Comparing Custom NER Models to spaCy’s NER
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for data professionals and software developers who want to create scalable data pipelines in Python
Led by Andrei Pruteanu, who have proven experience in delivering training focused on Python
Involves hands-on labs and interactive materials, promoting practical application of the concepts
Covers the latest version of Python libraries, ensuring relevance to current industry standards
Introduces advanced techniques such as Conditional Random Fields (CRFs) for building NER systems
Requires a basic understanding of Python programming and NLP concepts
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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 Creating Named Entity Recognition Systems with Python with these
activities:
Read and review 'Natural Language Processing with Python'
Show steps
Enhance your understanding of NLP concepts and Python libraries by reading and critically analyzing this comprehensive book.
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Read through the chapters of the book
-
Take notes on key concepts and techniques
-
Complete the exercises and assignments in the book
-
Summarize the main ideas of each chapter in your own words
Python coding and debugging exercises
Show steps
Practice writing and debugging Python code to refine your understanding of the language's syntax and structure.
Browse courses on
Python Coding
Show steps
-
Solve coding challenges on platforms like LeetCode or HackerRank
-
Work through the exercises in the course materials
-
Create your own Python scripts and test them
Build a Named Entity Recognition (NER) system
Show steps
Develop a functional NER system to apply your knowledge of Python and NLP libraries in a practical project.
Browse courses on
Named Entity Recognition
Show steps
-
Choose a specific domain or dataset for your NER system
-
Design the architecture of your NER system
-
Implement the NER system using Python and NLP libraries
-
Evaluate the performance of your NER system
Show all three activities
Read and review 'Natural Language Processing with Python'
Show steps
Enhance your understanding of NLP concepts and Python libraries by reading and critically analyzing this comprehensive book.
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Read through the chapters of the book
- Take notes on key concepts and techniques
- Complete the exercises and assignments in the book
- Summarize the main ideas of each chapter in your own words
Python coding and debugging exercises
Show steps
Practice writing and debugging Python code to refine your understanding of the language's syntax and structure.
Browse courses on
Python Coding
Show steps
Show steps
Show steps
- Solve coding challenges on platforms like LeetCode or HackerRank
- Work through the exercises in the course materials
- Create your own Python scripts and test them
Build a Named Entity Recognition (NER) system
Show steps
Develop a functional NER system to apply your knowledge of Python and NLP libraries in a practical project.
Browse courses on
Named Entity Recognition
Show steps
Show steps
Show steps
- Choose a specific domain or dataset for your NER system
- Design the architecture of your NER system
- Implement the NER system using Python and NLP libraries
- Evaluate the performance of your NER system
Show all three activities
Career center
Learners who complete Creating Named Entity Recognition Systems with Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are experts in extracting insights from data. They use their knowledge of statistics, machine learning, and programming to develop and implement algorithms that can identify patterns and make predictions. This course can help you develop the skills you need to become a successful Data Scientist, including how to use Python to create Named Entity Recognition systems. These systems can be used to identify specific classes of entities in texts, such as people, places, and organizations. This information can be used to improve a variety of applications, such as search engines, recommender systems, and fraud detection systems.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software systems. They use their knowledge of programming languages, data structures, and algorithms to create software that meets the needs of users. This course can help you develop the skills you need to become a successful Software Engineer, including how to use Python to create Named Entity Recognition systems. These systems can be used to improve the performance of a variety of applications, such as search engines, recommender systems, and fraud detection systems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and implementing machine learning models. They use their knowledge of machine learning algorithms, data science, and programming to create models that can make predictions and decisions. This course can help you develop the skills you need to become a successful Machine Learning Engineer, including how to use Python to create Named Entity Recognition systems. These systems can be used to improve the performance of a variety of applications, such as search engines, recommender systems, and fraud detection systems.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data. They use their knowledge of statistics, data mining, and programming to identify trends and patterns in data. This course can help you develop the skills you need to become a successful Data Analyst, including how to use Python to create Named Entity Recognition systems. These systems can be used to identify specific classes of entities in texts, such as people, places, and organizations. This information can be used to gain insights into customer behavior, market trends, and other important topics.
NLP Engineer
NLP Engineer
NLP Engineers are responsible for developing and implementing natural language processing systems. They use their knowledge of natural language processing, machine learning, and programming to create systems that can understand and generate human language. This course can help you develop the skills you need to become a successful NLP Engineer, including how to use Python to create Named Entity Recognition systems. These systems can be used to improve the performance of a variety of applications, such as search engines, recommender systems, and fraud detection systems.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. They use their knowledge of business analysis, data analysis, and programming to develop solutions that can improve efficiency and profitability. This course can help you develop the skills you need to become a successful Business Analyst, including how to use Python to create Named Entity Recognition systems. These systems can be used to identify specific classes of entities in texts, such as customer needs, market trends, and competitive threats. This information can be used to make better decisions and improve business outcomes.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. They use their knowledge of product management, marketing, and engineering to create products that meet the needs of users. This course can help you develop the skills you need to become a successful Product Manager, including how to use Python to create Named Entity Recognition systems. These systems can be used to improve the performance of a variety of products, such as search engines, recommender systems, and fraud detection systems.
Marketing Analyst
Marketing Analyst
Marketing Analysts are responsible for analyzing marketing data and identifying opportunities for improvement. They use their knowledge of marketing analytics, data analysis, and programming to develop insights that can improve marketing campaigns. This course can help you develop the skills you need to become a successful Marketing Analyst, including how to use Python to create Named Entity Recognition systems. These systems can be used to identify specific classes of entities in texts, such as customer needs, market trends, and competitive threats.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying and assessing risks. They use their knowledge of risk management, data analysis, and programming to develop strategies that can mitigate risks. This course can help you develop the skills you need to become a successful Risk Analyst, including how to use Python to create Named Entity Recognition systems. These systems can be used to identify specific classes of entities in texts, such as financial risks, operational risks, and compliance risks.
Information Security Analyst
Information Security Analyst
Information Security Analysts are responsible for protecting information assets. They use their knowledge of information security, data analysis, and programming to develop systems that can protect against cyber threats. This course can help you develop the skills you need to become a successful Information Security Analyst, including how to use Python to create Named Entity Recognition systems. These systems can be used to identify specific classes of entities in texts, such as security vulnerabilities, malicious code, and phishing attacks.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for developing and implementing quantitative models. They use their knowledge of mathematics, statistics, and programming to create models that can predict future outcomes. This course can help you develop the skills you need to become a successful Quantitative Analyst, including how to use Python to create Named Entity Recognition systems. These systems can be used to identify specific classes of entities in texts, such as financial trends, market risks, and economic indicators.
Cybersecurity Analyst
Cybersecurity Analyst
Cybersecurity Analysts are responsible for protecting computer systems and networks from cyber attacks. They use their knowledge of cybersecurity, data analysis, and programming to develop systems that can detect and prevent cyber threats. This course can help you develop the skills you need to become a successful Cybersecurity Analyst, including how to use Python to create Named Entity Recognition systems. These systems can be used to identify specific classes of entities in texts, such as malicious code, phishing attacks, and security vulnerabilities.
Data Mining Analyst
Data Mining Analyst
Data Mining Analysts are responsible for extracting insights from data. They use their knowledge of data mining, machine learning, and programming to develop algorithms that can identify patterns and make predictions. This course can help you develop the skills you need to become a successful Data Mining Analyst, including how to use Python to create Named Entity Recognition systems. These systems can be used to identify specific classes of entities in texts, such as customer needs, market trends, and competitive threats.
Statistical Modeler
Statistical Modeler
Statistical Modelers are responsible for developing and implementing statistical models. They use their knowledge of statistics, machine learning, and programming to create models that can predict future outcomes. This course can help you develop the skills you need to become a successful Statistical Modeler, including how to use Python to create Named Entity Recognition systems. These systems can be used to identify specific classes of entities in texts, such as financial trends, market risks, and economic indicators.
Software Developer
Software Developer
Software Developers are responsible for designing, developing, and maintaining software systems. They use their knowledge of programming languages, data structures, and algorithms to create software that meets the needs of users. This course may be useful to you if you are interested in becoming a Software Developer.
For more career information including salaries, visit:
OpenCourser.com/course/56pj6h/creating
Reading list
We've selected six 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
Creating Named Entity Recognition Systems with Python.
It is an essential resource for anyone interested in using Python for NLP. It provides a comprehensive overview of the field, including the latest techniques and best practices.
Covers the theory and practice of deep learning for NLP. It valuable resource for anyone interested in using deep learning for NLP.
Covers a wide range of topics in speech and language processing. It valuable resource for anyone interested in the field.
Primer on NLP. It provides a comprehensive overview of the field, including the latest techniques and best practices.
Comprehensive handbook on NLP. It covers a wide range of topics, from the basics to the latest research.
Save
Standard textbook on speech and language processing. It provides a comprehensive overview of the field, including the latest techniques and best practices.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/56pj6h/creating
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Time
93 hours
Level
Intermediate
Via
Pluralsight
Instructor
Andrei Pruteanu
Language
English
Good to know
Suitable for data professionals and software developers who want to create scalable data pipelines in Python
Led by Andrei Pruteanu, who have proven experience in delivering training focused on Python
Involves hands-on labs and interactive materials, promoting practical application of the concepts
Covers the latest version of Python libraries, ensuring relevance to current industry standards
Introduces advanced techniques such as Conditional Random Fields (CRFs) for building NER systems
Requires a basic understanding of Python programming and NLP concepts
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