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Hands On Natural Language Processing (NLP) using Python

4.4 Filled star Filled star Filled star Filled star Empty star
Based on 1,738 ratings
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Next Edge Coding

In this course you will learn the various concepts of natural language processing by implementing them hands on in python programming language. This course is completely project based and from the start of the course the main objective would be to learn all the concepts required to finish the different projects. You will be building a text classifier which you will use to predict sentiments of tweets in real time and you will also be building an article summarizer which will fetch articles from websites and find the summary. Apart from these you will also be doing a lot of mini projects through out the course. So, at the end of the course you will have a deep understanding of NLP and how it is applied in real world.

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What's inside

Learning objectives

  • Understand the various concepts of natural language processing along with their implementation
  • Build natural language processing based applications
  • Learn about the different modules available in python for nlp
  • Create personal spam filter or sentiment predictor
  • Create personal text summarizer

Syllabus

Introduction to the Course
What is NLP?
Getting the Course Resources
Getting the Course Resources - Text
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Examines NLP, its applications, and implementation in Python, catering to students with some programming experience
Emphasizes hands-on implementation in Python, making it suitable for learners who want practical experience in NLP
Provides a comprehensive overview of NLP concepts and techniques, offering a strong foundation for learners
Uses industry-standard tools and libraries like NLTK, Pandas, and Numpy, preparing learners for real-world applications
Covers advanced NLP techniques such as Word2Vec, Word Embeddings, and Latent Semantic Analysis
Requires learners to have a basic understanding of Python, potentially limiting accessibility for absolute beginners

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Reviews summary

Practical nlp with python for real-world projects

According to learners, this course offers a largely positive and hands-on approach to Natural Language Processing using Python. Many appreciate the project-based learning, which provides practical application of concepts and helps build a portfolio of real-world NLP projects. Students particularly praise the clear explanations and engaging teaching style of the instructor. While the initial Python sections are beneficial for beginners, some with prior coding experience found them to be a review of fundamentals. The course effectively covers core NLP concepts and libraries, with a strong focus on building functional applications like sentiment predictors and text summarizers. Recent reviews indicate the course has been kept up-to-date, ensuring code functionality.
Course includes Python fundamentals, aiding newer learners.
"As someone fairly new to Python, the initial sections on Python basics were incredibly helpful and a great refresher."
"I appreciated that the course starts with Python fundamentals, it really sets the stage for the NLP topics."
"It’s great for beginners, as it covers Python basics before diving into NLP, which is a big plus for me."
Course content and code examples are current and functional.
"The course seems to be well-maintained and the code works perfectly, which is crucial for a hands-on NLP course."
"Glad to see that the course material and libraries used are up-to-date, unlike some other older NLP courses."
"I didn't encounter any issues with outdated code or broken libraries, which made the learning experience smooth."
Instructor provides clear explanations of complex NLP concepts.
"The instructor explains each concept clearly and concisely, making complex topics easy to grasp."
"I found the explanations to be very clear, which really helped me grasp the concepts of tokenization and stemming."
"The instructor's approach to teaching is excellent, breaking down difficult ideas into understandable segments."
Develops practical skills for real-world NLP applications.
"This course truly helps in developing practical skills, enabling me to build useful NLP applications."
"I now feel confident in my ability to apply NLP concepts to real-world problems, thanks to this course."
"The skills I've learned here are directly applicable to my job and helped me understand real-time data processing."
Strong emphasis on practical application through engaging projects.
"This course is truly hands-on, the projects are practical and helped me understand how NLP is applied in the real world."
"The project-based learning is the strongest aspect; I really enjoyed building the sentiment analyzer and text summarizer."
"I gained so much from the hands-on coding and actual implementations. It's not just theory, it's practical application."
Less depth for experienced learners seeking advanced topics.
"While great for practical application, I felt it could use more in-depth coverage on complex NLP algorithms or advanced techniques."
"For an intermediate Python developer, the initial sections felt a bit too basic and slow-paced."
"I wished for a deeper dive into the theoretical underpinnings of some of the models, but it's more about hands-on."

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 Hands On Natural Language Processing (NLP) using Python with these activities:
Practice tokenization and stemming
Practice tokenization and stemming in advance of taking the course to strengthen your foundational understanding of NLP and reduce time spent on these topics in the course.
Browse courses on Tokenization
Show steps
  • Read the course syllabus and identify sections that cover tokenization and stemming.
  • Identify online resources and tutorials that provide hands-on exercises for tokenization and stemming
  • Practice tokenizing text using different techniques, such as word-based and character-based tokenization.
  • Practice stemming words using different algorithms, such as the Porter stemmer and the Lancaster stemmer.
Complete NLTK Tutorial Exercises
Work through the exercises in the NLTK tutorial to gain hands-on experience with NLP techniques.
Browse courses on Natural Language Processing
Show steps
  • Set up your Python environment and install NLTK.
  • Work through the NLTK tutorial exercises.
  • Experiment with different NLP techniques.
Compile a List of NLP Resources
Gather and organize a collection of valuable resources, such as tutorials, articles, and tools, to support your NLP learning journey.
Browse courses on Natural Language Processing
Show steps
  • Research and identify reputable NLP resources.
  • Organize the resources into a central location, such as a document or website.
  • Share the resource compilation with others.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice Writing Regular Expressions
Write regular expressions to match patterns in text, such as extracting data from unstructured text or validating user input.
Browse courses on Python
Show steps
  • Review basic concepts of regular expressions.
  • Practice writing basic regular expressions to match patterns.
  • Test your regular expressions on different text samples.
  • Apply regular expressions to a real-world task.
Join a Natural Language Processing Study Group
Connect with other learners and discuss NLP concepts, share resources, and work on projects together.
Browse courses on Natural Language Processing
Show steps
  • Find a study group or start your own.
  • Meet regularly to discuss NLP topics.
  • Collaborate on NLP projects.
Read 'Natural Language Processing with Python' by Steven Bird et al.
Gain a comprehensive understanding of NLP concepts and techniques through this foundational book.
View Natural Language Processing with Python:... on Amazon
Show steps
  • Purchase or borrow the book.
  • Read the book thoroughly.
  • Take notes and highlight important concepts.
  • Complete the exercises provided in the book.
Create a Text Classification Model
Build a model to classify text into different categories, such as sentiment analysis or spam detection.
Browse courses on Machine Learning
Show steps
  • Collect and prepare a dataset of labeled text data.
  • Choose and train a machine learning model for text classification.
  • Evaluate the performance of the model.
  • Deploy the model for real-world use.
Attend a Natural Language Processing Workshop
Deepen your understanding of NLP by attending a workshop led by industry experts.
Browse courses on Natural Language Processing
Show steps
  • Find an NLP workshop that aligns with your interests and skill level.
  • Attend the workshop and actively participate in the sessions.
  • Apply the knowledge and skills gained from the workshop to your own projects.
Develop a Text Summarization Tool
Build a tool to automatically summarize text documents, extracting the key points and main ideas.
Browse courses on Natural Language Processing
Show steps
  • Research and understand different text summarization techniques.
  • Design and implement a text summarization algorithm.
  • Test and evaluate the performance of the tool.
  • Package and deploy the tool for real-world use.

Career center

Learners who complete Hands On Natural Language Processing (NLP) using Python will develop knowledge and skills that may be useful to these careers:
Natural Language Processing Engineer
This course is directly relevant to the role of a Natural Language Processing Engineer, as it provides a foundation in the field and covers essential concepts such as text classification, text summarization, and word vectors. The hands-on projects would also be valuable experience.
See salaries and explore the career path for Natural Language Processing Engineer
Computational Linguist
A Computational Linguist would find the course's coverage of natural language processing concepts and Python programming to be particularly useful. The course's emphasis on projects, such as building a text classifier and article summarizer, would also be relevant experience.
See salaries and explore the career path for Computational Linguist
Machine Learning Engineer
The course's coverage of natural language processing concepts and Python programming would be useful for a Machine Learning Engineer, as this role often involves working with natural language data. The course's projects, such as building a text classifier and article summarizer, would also be relevant experience.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
A Data Scientist would benefit from the course given its emphasis on building natural language processing-based applications, as that skill is essential in the field. The course's hands-on approach and focus on projects are also well aligned with the practical nature of the role.
See salaries and explore the career path for Data Scientist
Data Analyst
A Data Analyst would benefit from the course's coverage of natural language processing concepts and Python programming, as these skills are increasingly important in the field. The course's focus on projects, such as building a text classifier and article summarizer, would also be valuable experience.
See salaries and explore the career path for Data Analyst
Software Engineer
The course's coverage of Python programming and natural language processing concepts would be beneficial for a Software Engineer who works on projects involving natural language data. The course's emphasis on building applications would also be relevant experience.
See salaries and explore the career path for Software Engineer
Product Manager
The course may be useful for a Product Manager who works on products that involve natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.
See salaries and explore the career path for Product Manager
Business Analyst
The course may be useful for a Business Analyst who works on projects involving natural language data, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.
See salaries and explore the career path for Business Analyst
Technical Writer
The course may be useful for a Technical Writer who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.
See salaries and explore the career path for Technical Writer
Content Strategist
The course may be useful for a Content Strategist who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.
See salaries and explore the career path for Content Strategist
Marketing Manager
The course may be useful for a Marketing Manager who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.
See salaries and explore the career path for Marketing Manager
Sales Manager
The course may be useful for a Sales Manager who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.
See salaries and explore the career path for Sales Manager
Customer Success Manager
The course may be useful for a Customer Success Manager who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.
See salaries and explore the career path for Customer Success Manager
Operations Manager
The course may be useful for an Operations Manager who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.
See salaries and explore the career path for Operations Manager
Project Manager
The course may be useful for a Project Manager who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.
See salaries and explore the career path for Project Manager

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 Hands On Natural Language Processing (NLP) using Python.
Cover image
Natural Language Processing with Python
Save
Provides a comprehensive overview of natural language processing, covering a wide range of topics from basic concepts to advanced techniques. It valuable resource for both beginners and experienced practitioners.
Natural Language Processing with Python: Analyzing...
Paperback
$$$
Natural Language Processing with Python: Analyzing...
Kindle Edition
$$
Cover image
Speech and Language Processing. An Introduction to...
Save
Classic textbook on speech and language processing. It provides a comprehensive overview of the field, from the basics of speech production and perception to advanced topics such as natural language understanding and generation.
Speech and Language Processing An Introduction to...
Paperback
$$
Cover image
Natural Language Processing with Python
Save
Is an introduction to the Natural Language Toolkit (NLTK), a popular open-source library for natural language processing. It covers a wide range of topics, from the basics of natural language processing to advanced techniques such as machine learning and deep learning.
Natural Language Processing with Python: Analyzing...
Paperback
$$$
(中文) Python Natural Language Processing(Chinese Edition)
Paperback
$$
Natural Language Processing with Python: Analyzing...
Kindle Edition
$$
Cover image
Natural Language Processing in Action, Second...
Save
Provides a practical introduction to natural language processing. It covers a wide range of topics, from the basics of natural language processing to advanced techniques such as machine learning and deep learning.
Natural Language Processing in Action, Second...
Paperback
$$$
Cover image
Machine Learning Q and AI
Save
Provides a practical introduction to machine learning for natural language processing. It covers a wide range of topics, from the basics of machine learning to advanced techniques such as deep learning.
Machine Learning Q and AI: 30 Essential Questions...
Paperback
$$
Machine Learning Q and AI: 30 Essential Questions...
Kindle Edition
$$
Cover image
Practical Natural Language Processing
Save
Provides a practical introduction to natural language processing in Python. It covers a wide range of topics, from the basics of natural language processing to advanced techniques such as machine learning and deep learning.
Practical Natural Language Processing: A...
Paperback
$$$
Practical Natural Language Processing: A...
Kindle Edition
$$
Cover image
TensorFlow in Action
Save
Provides a comprehensive overview of natural language processing with transformers. It covers a wide range of topics, from the basics of transformers to advanced techniques such as attention mechanisms and self-supervised learning.
TensorFlow in Action
Paperback
$$$
TensorFlow in Action
Kindle Edition
$$$
Cover image
Cross-Lingual Word Embeddings
Save
Provides a comprehensive overview of deep learning for natural language processing. It covers a wide range of topics, from the basics of deep learning to advanced techniques such as attention mechanisms and transformer networks.
Cross-Lingual Word Embeddings (Synthesis Lectures...
Paperback
$$
Pattern Recognition and Machine Learning
Save
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, from the basics of probability and statistics to advanced techniques such as deep learning.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$

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Effort
10.5 total hours
Via
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Instructor
Next Edge Coding
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Examines NLP, its applications, and implementation in Python, catering to students with some programming experience
Emphasizes hands-on implementation in Python, making it suitable for learners who want practical experience in NLP
Provides a comprehensive overview of NLP concepts and techniques, offering a strong foundation for learners
Uses industry-standard tools and libraries like NLTK, Pandas, and Numpy, preparing learners for real-world applications
Covers advanced NLP techniques such as Word2Vec, Word Embeddings, and Latent Semantic Analysis
Requires learners to have a basic understanding of Python, potentially limiting accessibility for absolute beginners
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