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Introduction to Natural Language Processing in Python
Charles Ivan Niswander II
Charles Ivan Niswander II
In this 1-hour long project-based course, you will learn basic principles of Natural Language Processing, or NLP. NLP refers to a group of methods for parsing and extracting meaning from human language. In this course, we'll explore the basics of NLP as well...
Read more
In this 1-hour long project-based course, you will learn basic principles of Natural Language Processing, or NLP. NLP refers to a group of methods for parsing and extracting meaning from human language. In this course, we'll explore the basics of NLP as well...
Read more
In this 1-hour long project-based course, you will learn basic principles of Natural Language Processing, or NLP. NLP refers to a group of methods for parsing and extracting meaning from human language. In this course, we'll explore the basics of NLP as well as detail the workflow pipeline for NLP and define the three basic approaches to NLP tasks. You'll get the chance to go hands on with a variety of methods for coding NLP tasks ranging from stemming and chunking, Named Entity Recognition, lemmatization, and other tokenization methods. You'll be introduced to open-source libraries such as NLTK, spaCy, Gensim, Pattern, and TextBlob. By the end of this course, you will feel more acquainted with the basics of the NLP workflow and will be ready to begin experimenting and prepare for production-level NLP application coding.
I would encourage learners to experiment with the tools and methods discussed in this course. The learner is highly encouraged to experiment beyond the scope of the course.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/yaoofj/introduction
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops the foundations of Natural Language Processing, a field relevant to fields such as linguistics, machine learning, and data science
Introduces open-source libraries, which are essential tools for NLP tasks
Hands-on exercises provide practical experience with NLP methods
Provides a structured workflow pipeline for NLP tasks
Taught by Charles Ivan Niswander II, a recognized expert in NLP
Encourages experimentation beyond the course scope, fostering independent learning
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Reviews summary
Intro to nlp in python
This course provides an introduction to the basics of Natural Language Processing (NLP). Students will explore the NLP workflow pipeline and learn about basic NLP tasks such as stemming, chunking, Named Entity Recognition, lemmatization, and other tokenization methods. The course uses open-source libraries such as NLTK, spaCy, Gensim, Pattern, and TextBlob. While the course provides a basic overview of NLP, reviewers have expressed frustration with the lack of clear explanations and poor organization. Many reviewers found that the course was not helpful and would not recommend it to others.
Course provides hands-on practice with NLP methods.
"You'll get the chance to go hands on with a variety of methods for coding NLP tasks ranging from stemming and chunking, Named Entity Recognition, lemmatization, and other tokenization methods."
Rhyme tool forces full-screen mode and overlapping, preventing note-taking.
"Get rid of rhyme. Rhyme is a terrible tool, despite its good intention. While I was doing the project, Rhyme always forces the recording to be viewed in expand mode (or full screen) with 110% zoom in. This causes the recording to overlap my working screen, preventing me from simultaneously following the instructions."
Lack of clear explanations and poor organization hinder learning.
"Wrong Code Snippets and very poor explanation!"
"Very bad explanations, no possibility to copy files"
"Not helpful. Not enough explanation in the coding part"
"The instructor makes several mistakes and does not even see that his code does not what it should."
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 Introduction to Natural Language Processing in Python with these
activities:
Organize Course Materials
Show steps
Keep your course materials organized to enhance your learning and review process.
Show steps
-
Gather and organize course materials, including lecture notes, assignments, and other resources.
-
Create a system for organizing and retrieving materials easily.
-
Regularly review and update your organized materials.
Review Python
Show steps
Brush up on the fundamentals of Python programming before starting this course to ensure a solid foundation.
Browse courses on
Python Programming Language
Show steps
-
Revisit the basics of Python programming, such as data types, variables, and control flow.
-
Practice writing simple Python programs to reinforce your understanding.
Explore NLP Tools and Libraries
Show steps
Familiarize yourself with popular NLP tools and libraries to enhance your learning.
Show steps
-
Research and identify NLP tools and libraries relevant to the course topics.
-
Follow guided tutorials to learn how to use these tools effectively.
-
Experiment with the tools to gain hands-on experience.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read 'Natural Language Processing with Python'
Show steps
Supplement your learning with this comprehensive book that provides an overview of NLP concepts and techniques.
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Read the chapters relevant to the topics covered in the course.
Join a Study Group
Show steps
Collaborate with peers to reinforce your understanding and enhance your learning experience.
Show steps
-
Find a group of students in the course who share similar interests and goals.
-
Meet regularly to discuss course material, share insights, and work on assignments.
Solve NLP Coding Challenges
Show steps
Enhance your NLP coding skills by solving practice problems on online platforms or in textbooks.
Browse courses on
Natural Language Processing
Show steps
-
Identify a set of NLP coding challenges aligned with the concepts covered in the course.
-
Attempt to solve the challenges using the NLP techniques learned in the course.
-
Review your solutions and compare them to provided solutions or consult with peers.
Create NLP Visualizations
Show steps
Enhance your understanding of NLP concepts by visualizing the data and results of your NLP analysis.
Browse courses on
Natural Language Processing
Show steps
-
Identify key NLP concepts and techniques to visualize.
-
Use data visualization tools or libraries to create clear and informative visualizations.
-
Analyze and interpret the visualizations to gain insights into the NLP results.
Develop an NLP Project
Show steps
Apply your NLP knowledge by creating a project that leverages NLP techniques to solve a specific problem.
Browse courses on
Natural Language Processing
Show steps
-
Identify a project idea that aligns with your interests and the course material.
-
Design and implement your project using the NLP techniques learned in the course.
-
Document and present your project to showcase your understanding of NLP.
-
Consider sharing your project on platforms such as GitHub or Kaggle.
Organize Course Materials
Show steps
Keep your course materials organized to enhance your learning and review process.
Show steps
Show steps
Show steps
- Gather and organize course materials, including lecture notes, assignments, and other resources.
- Create a system for organizing and retrieving materials easily.
- Regularly review and update your organized materials.
Review Python
Show steps
Brush up on the fundamentals of Python programming before starting this course to ensure a solid foundation.
Browse courses on
Python Programming Language
Show steps
Show steps
Show steps
- Revisit the basics of Python programming, such as data types, variables, and control flow.
- Practice writing simple Python programs to reinforce your understanding.
Explore NLP Tools and Libraries
Show steps
Familiarize yourself with popular NLP tools and libraries to enhance your learning.
Show steps
Show steps
Show steps
- Research and identify NLP tools and libraries relevant to the course topics.
- Follow guided tutorials to learn how to use these tools effectively.
- Experiment with the tools to gain hands-on experience.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read 'Natural Language Processing with Python'
Show steps
Supplement your learning with this comprehensive book that provides an overview of NLP concepts and techniques.
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Read the chapters relevant to the topics covered in the course.
Join a Study Group
Show steps
Collaborate with peers to reinforce your understanding and enhance your learning experience.
Show steps
Show steps
Show steps
- Find a group of students in the course who share similar interests and goals.
- Meet regularly to discuss course material, share insights, and work on assignments.
Solve NLP Coding Challenges
Show steps
Enhance your NLP coding skills by solving practice problems on online platforms or in textbooks.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify a set of NLP coding challenges aligned with the concepts covered in the course.
- Attempt to solve the challenges using the NLP techniques learned in the course.
- Review your solutions and compare them to provided solutions or consult with peers.
Create NLP Visualizations
Show steps
Enhance your understanding of NLP concepts by visualizing the data and results of your NLP analysis.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify key NLP concepts and techniques to visualize.
- Use data visualization tools or libraries to create clear and informative visualizations.
- Analyze and interpret the visualizations to gain insights into the NLP results.
Develop an NLP Project
Show steps
Apply your NLP knowledge by creating a project that leverages NLP techniques to solve a specific problem.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify a project idea that aligns with your interests and the course material.
- Design and implement your project using the NLP techniques learned in the course.
- Document and present your project to showcase your understanding of NLP.
- Consider sharing your project on platforms such as GitHub or Kaggle.
Career center
Learners who complete Introduction to Natural Language Processing in Python will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Scientist
Natural Language Processing Scientist
An NLP scientist researches and develops systems to understand and generate human language. Their main goal is to enable smooth communication between humans and machines. This course may help you enter this career by introducing you to NLP systems and techniques. You will also gain proficiency in using NLP tools and libraries, which will be valuable for your work as an NLP scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. NLP is a subfield of machine learning, so this course can be helpful in learning the basics of NLP and how to apply it in the context of machine learning. You will also gain experience with NLP tools and libraries, which will be useful in your work as a Machine Learning Engineer.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. NLP is a valuable tool for data scientists, as it can be used to extract insights from text data. This course can help you gain a foundation in NLP and learn how to apply it to data science problems. You will also gain experience with NLP tools and libraries, which will be useful in your work as a Data Scientist.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. NLP is becoming increasingly important in software development, as it can be used to improve the user experience and automate tasks. This course can help you gain a foundation in NLP and learn how to apply it to software development. You will also gain experience with NLP tools and libraries, which will be useful in your work as a Software Engineer.
Analyst
Analyst
Analysts use data to understand trends and patterns. NLP can be a valuable tool for analysts, as it can be used to extract insights from text data. This course can help you gain a foundation in NLP and learn how to apply it to data analysis. You will also gain experience with NLP tools and libraries, which will be useful in your work as an Analyst.
Information Architect
Information Architect
Information Architects design and organize information systems. NLP can be a valuable tool for information architects, as it can be used to improve the findability and accessibility of information. This course can help you gain a foundation in NLP and learn how to apply it to information architecture. You will also gain experience with NLP tools and libraries, which will be useful in your work as an Information Architect.
User Experience Designer
User Experience Designer
User Experience Designers design and evaluate the user experience of products and services. NLP can be a valuable tool for UX designers, as it can be used to understand user needs and preferences. This course can help you gain a foundation in NLP and learn how to apply it to UX design. You will also gain experience with NLP tools and libraries, which will be useful in your work as a UX Designer.
Content Strategist
Content Strategist
Content Strategists plan and create content for websites, blogs, and other platforms. NLP can be a valuable tool for content strategists, as it can be used to analyze and understand customer needs and preferences. This course can help you gain a foundation in NLP and learn how to apply it to content strategy. You will also gain experience with NLP tools and libraries, which will be useful in your work as a Content Strategist.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns. NLP can be a valuable tool for marketing managers, as it can be used to understand customer needs and preferences. This course can help you gain a foundation in NLP and learn how to apply it to marketing. You will also gain experience with NLP tools and libraries, which will be useful in your work as a Marketing Manager.
Sales Manager
Sales Manager
Sales Managers lead and manage sales teams. NLP can be a valuable tool for sales managers, as it can be used to understand customer needs and preferences. This course can help you gain a foundation in NLP and learn how to apply it to sales management. You will also gain experience with NLP tools and libraries, which will be useful in your work as a Sales Manager.
Customer Success Manager
Customer Success Manager
Customer Success Managers help customers achieve success with a company's products and services. NLP can be a valuable tool for customer success managers, as it can be used to understand customer needs and preferences. This course can help you gain a foundation in NLP and learn how to apply it to customer success management. You will also gain experience with NLP tools and libraries, which will be useful in your work as a Customer Success Manager.
Product Manager
Product Manager
Product Managers plan and manage the development of products. NLP can be a valuable tool for product managers, as it can be used to understand customer needs and preferences. This course can help you gain a foundation in NLP and learn how to apply it to product management. You will also gain experience with NLP tools and libraries, which will be useful in your work as a Product Manager.
Project Manager
Project Manager
Project Managers plan and manage projects. NLP can be a valuable tool for project managers, as it can be used to manage and track project progress. This course can help you gain a foundation in NLP and learn how to apply it to project management. You will also gain experience with NLP tools and libraries, which will be useful in your work as a Project Manager.
Business Analyst
Business Analyst
Business Analysts analyze and solve business problems. NLP can be a valuable tool for business analysts, as it can be used to extract insights from text data. This course can help you gain a foundation in NLP and learn how to apply it to business analysis. You will also gain experience with NLP tools and libraries, which will be useful in your work as a Business Analyst.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation. NLP can be a valuable tool for technical writers, as it can be used to generate and format documentation. This course can help you gain a foundation in NLP and learn how to apply it to technical writing. You will also gain experience with NLP tools and libraries, which will be useful in your work as a Technical Writer.
For more career information including salaries, visit:
OpenCourser.com/course/yaoofj/introduction
Reading list
We've selected 11 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
Introduction to Natural Language Processing in Python.
This textbook covers the foundations of NLP, including tokenization, stemming, lemmatization, parsing, and machine learning for NLP. It comprehensive resource for beginners and experienced NLP practitioners alike.
Save
This textbook provides a comprehensive overview of speech and language processing, covering topics such as acoustics, phonetics, phonology, morphology, syntax, semantics, and pragmatics. It valuable resource for students and researchers in NLP and related fields.
This textbook provides a comprehensive overview of speech recognition, covering topics such as акустика, phonetics, phonology, morphology, syntax, semantics, and pragmatics. It valuable resource for students and researchers interested in the theoretical and practical aspects of speech recognition.
This handbook provides a comprehensive overview of NLP, covering topics such as text preprocessing, feature engineering, machine learning for NLP, and deep learning for NLP. It valuable resource for students and researchers interested in a broad overview of the field.
This textbook focuses on the computational aspects of NLP, covering topics such as natural language generation, machine translation, and question answering. It valuable resource for students and researchers interested in the theoretical and practical aspects of NLP.
Provides an introduction to the Natural Language Toolkit (NLTK), a popular NLP library for Python. It valuable resource for students and researchers interested in using NLTK for NLP tasks.
This textbook provides a comprehensive overview of machine learning for NLP, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for students and researchers interested in the application of machine learning to NLP tasks.
This textbook focuses on deep learning for NLP, covering topics such as recurrent neural networks, convolutional neural networks, and transformer networks. It valuable resource for students and researchers interested in the application of deep learning to NLP tasks.
This textbook focuses on statistical machine translation, covering topics such as machine translation models, machine translation training, and machine translation evaluation. It valuable resource for students and researchers interested in the application of statistical machine translation to NLP tasks.
This textbook provides a comprehensive overview of information retrieval, covering topics such as text preprocessing, indexing, ranking, and evaluation. It valuable resource for students and researchers interested in the theoretical and practical aspects of information retrieval.
This textbook provides a comprehensive overview of the statistical foundations of NLP, covering topics such as probability, statistics, and machine learning. It valuable resource for students and researchers interested in the theoretical and practical aspects of NLP.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yaoofj/introduction
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Charles Ivan Niswander II
Good to know
Develops the foundations of Natural Language Processing, a field relevant to fields such as linguistics, machine learning, and data science
Introduces open-source libraries, which are essential tools for NLP tasks
Hands-on exercises provide practical experience with NLP methods
Provides a structured workflow pipeline for NLP tasks
Taught by Charles Ivan Niswander II, a recognized expert in NLP
Encourages experimentation beyond the course scope, fostering independent learning
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