We may earn an affiliate commission when you visit our partners.
Course image
AWS Instructor

Amazon Comprehend is a natural-language processing (NLP) service that you can use to extract valuable insights and connections from text. In this "Getting Started" course, you will learn about the benefits, features, typical use cases, technical concepts, and costs of Amazon Comprehend. You will have the opportunity to review an architecture for a text-processing solution that uses Amazon Comprehend, which you can adapt to your own use cases. Through a guided tutorial consisting of a narrated video, step-by-step instructions and transcript, you will also try the service in your own Amazon Web Services (AWS) account.

Read more

Amazon Comprehend is a natural-language processing (NLP) service that you can use to extract valuable insights and connections from text. In this "Getting Started" course, you will learn about the benefits, features, typical use cases, technical concepts, and costs of Amazon Comprehend. You will have the opportunity to review an architecture for a text-processing solution that uses Amazon Comprehend, which you can adapt to your own use cases. Through a guided tutorial consisting of a narrated video, step-by-step instructions and transcript, you will also try the service in your own Amazon Web Services (AWS) account.

In lieu of using your own account, you can become an AWS Skill Builder subscriber to unlock all our hands-on, interactive content, including unlimited access to 125+ AWS Builder Labs. These hands-on, guided labs allow you to develop practical skills for common cloud scenarios by building in an AWS sandbox environment without the risk of accruing unwanted charges.

Enroll now

What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Meets an industry need by teaching skills and knowledge relevant to text-processing
Provides an opportunity to experiment with the service through a guided tutorial
Belongs to a series of courses that may provide a comprehensive study of topics

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Essential introduction to amazon comprehend

According to students, this course provides a clear and practical introduction to Amazon Comprehend. Learners consistently praise its ability to offer a solid foundational understanding of the NLP service, making it ideal for beginners. The guided tutorials and hands-on labs are highlighted as particularly helpful for gaining practical experience. While it serves as an excellent starting point, students acknowledge its introductory scope, focusing on core concepts and typical use cases rather than advanced topics. It effectively equips learners with the basic knowledge to explore AWS NLP services further.
A good overview, but not an in-depth technical deep dive.
"Remember it's a 'getting started' course, so it covers basics and use cases, not advanced intricacies."
"I was hoping for more on custom models, but I understand that's beyond this course's intended scope."
"For a quick, concise overview of what Amazon Comprehend can do, it’s excellent, but plan for follow-up studies."
Perfectly suited for those new to AWS NLP services.
"As a complete beginner to NLP and AWS Comprehend, I found this course very accessible and easy to follow."
"No prior deep NLP or AWS experience was needed to grasp the core concepts, which was a huge plus."
"It sets a good, comfortable foundation, preparing me well before diving into more advanced topics."
Offers valuable practical experience with the service.
"The guided tutorial was incredibly helpful for actually trying the service in my AWS account."
"Being able to use an AWS Skill Builder lab made learning tangible and risk-free, which I appreciated."
"I valued the step-by-step instructions for getting started, it made implementation straightforward."
Provides a foundational understanding of Amazon Comprehend.
"This course is a great entry point to understand Amazon Comprehend's capabilities, very well-structured."
"I found the explanations of features, use cases, and costs remarkably clear and easy to follow."
"It really demystified the basics of NLP with AWS, giving me a solid conceptual grasp."

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 Getting Started with Amazon Comprehend with these activities:
Review NLP Fundamentals
Refreshing your knowledge of NLP fundamentals can help you prepare for this course and improve your understanding of the concepts covered.
Show steps
  • Review your notes from previous NLP courses or tutorials.
  • Read articles and blog posts about NLP.
  • Watch videos about NLP.
Find an NLP Mentor
Finding a mentor can provide you with guidance and support as you learn NLP, which can help you achieve better learning outcomes in this course.
Show steps
  • Attend NLP meetups and conferences.
  • Reach out to NLP professionals on LinkedIn.
  • Ask your professor or classmates for recommendations.
Review NLP Techniques
This book is a comprehensive guide to NLP techniques and will help you understand the fundamentals of NLP before taking this course.
Show steps
  • Read chapters 1-3 of the book.
  • Complete the exercises at the end of each chapter.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Refresher on Python basic syntax and data structures
Review the fundamentals of Python to ensure a solid foundation for the course.
Browse courses on Python Programming
Show steps
  • Review variables, data types, and operators.
  • Practice basic data structures like lists, tuples, and dictionaries.
Complete the NLP with Python Tutorial
This tutorial will provide you with a hands-on introduction to NLP with Python, which will be helpful for understanding the concepts covered in this course.
Show steps
  • Follow the steps in the tutorial.
  • Complete the exercises at the end of each section.
Join a study group to discuss Amazon Comprehend
Engage with fellow learners and exchange knowledge through a study group.
Browse courses on Amazon Comprehend
Show steps
  • Find a study group or create one with fellow course participants.
  • Set up regular meetings to discuss course content and share insights.
  • Collaborate on projects and assignments.
Create an NLP Resource Compilation
Creating a compilation of NLP resources can help you organize and review the materials you need for this course, which can contribute to better learning outcomes.
Show steps
  • Gather NLP resources from a variety of sources.
  • Organize the resources into a coherent structure.
  • Make the compilation available to other students.
Complete the tutorial on Amazon Comprehend's Key Phrase Extraction feature
Dive deeper into a specific feature of Amazon Comprehend through a guided tutorial.
Browse courses on Amazon Comprehend
Show steps
  • Access the Amazon Comprehend tutorial documentation.
  • Follow the step-by-step instructions to extract key phrases from sample text.
  • Experiment with different text inputs and observe the results.
Complete the NLP Practice Drills
These practice drills will help you reinforce your understanding of NLP concepts and techniques, which will be beneficial for your success in this course.
Show steps
  • Complete the drills in order.
  • Review your answers and identify areas where you need more practice.
Practice NLP tasks using Amazon Comprehend's interactive console
Gain hands-on experience with Amazon Comprehend's NLP capabilities.
Browse courses on Amazon Comprehend
Show steps
  • Log in to your AWS account and open the Amazon Comprehend console.
  • Select the 'Try it now' option to access the interactive console.
  • Experiment with different NLP tasks, such as sentiment analysis and named entity recognition.
Create an NLP Project
This project will allow you to apply your NLP skills to a real-world problem, which will help you deepen your understanding of NLP and its applications.
Show steps
  • Choose a project idea.
  • Gather the necessary data.
  • Develop and implement your NLP solution.
  • Evaluate your results.
  • Write a report on your project.
Participate in an NLP Competition
Participating in a competition can help you test your NLP skills and knowledge, which can motivate you to learn more and achieve better results in this course.
Show steps
  • Find an NLP competition that is appropriate for your skill level.
  • Form a team or work on your own.
  • Develop and implement your NLP solution.
  • Submit your solution to the competition.
Build a simple web app that utilizes Amazon Comprehend's API
Apply your skills by creating a practical application that leverages Amazon Comprehend's API.
Browse courses on Amazon Comprehend
Show steps
  • Design the architecture of your web app and choose a programming language.
  • Integrate Amazon Comprehend's API into your code.
  • Deploy your web app and test its functionality.

Career center

Learners who complete Getting Started with Amazon Comprehend will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 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 Getting Started with Amazon Comprehend.
Provides a comprehensive overview of natural language processing techniques for entity extraction from text. It covers a wide range of topics, including rule-based methods, machine learning methods, and hybrid methods. This book must-have for anyone interested in learning about the latest advances in entity extraction.
Is widely considered the 'bible' of speech and language processing and must-have for anyone interested in this field. It includes a comprehensive overview of language detection.
Considered a key book specifically focused on sentiment analysis, this resource offers a comprehensive introduction from an NLP perspective. It covers core areas, including opinion mining, and has been updated to include recent deep learning methods. is valuable for researchers and practitioners and serves as a great introductory and reference book.
The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.
A timeless mathematical masterpiece that provides an uncompromising Bayesian and probabilistic foundation for linear classifiers, graphical models, kernel methods, and sampling algorithms.
The definitive mathematical and theoretical canon of statistical learning, establishing the rigorous analytical foundations of regularized regression, tree ensembles, and high-dimensional inference.
A distilled practitioner field guide outlining best practices for the entire machine learning lifecycle, from problem formulation and feature engineering to model deployment and monitoring.
This revised edition focuses specifically on using the Hugging Face Transformers library, which is widely used for state-of-the-art NLP models, including those for sentiment analysis. It highly practical book for implementing modern sentiment analysis techniques.
Delivers an end-to-end systems architecture guide for building reliable production ML systems, addressing data engineering trade-offs, real-time feature computation, and model drift mitigation.
Focuses specifically on web scraping, a key method for extracting data from websites. It covers various Python libraries and techniques for gathering and processing web data. It's a practical guide for those interested in extracting data from online sources and is suitable for beginners and those looking to deepen their web scraping skills.
Bridges linear algebra, multivariable calculus, vector geometry, and probability directly to fundamental machine learning algorithms like PCA, Gaussian mixture models, and support vector machines.
Focuses on sentiment analysis specifically within the context of social networks, addressing the unique challenges of analyzing large-scale, noisy, and dynamic text data from platforms like Twitter and Facebook. It explores both semantic and machine learning approaches and provides insights into opinion spamming and social network analysis.
Given the increasing importance of transformers and pre-trained models like BERT in modern NLP, including sentiment analysis, this book is highly relevant for understanding these contemporary techniques. It provides a practical guide to building NLP applications using these architectures.
Provides the monumental mathematical foundation of modern neural networks, connecting deep representation learning, backpropagation dynamics, and sequence modeling to empirical machine learning.
Offers a practical approach to building NLP systems, covering the entire project lifecycle. It is highly relevant for those looking to apply NLP concepts in real-world scenarios, which aligns with the practical nature of using a service like Amazon Comprehend. It bridges the gap between theory and application and is suitable for practitioners.
Covers the latest AWS certification exam blueprint and provides comprehensive coverage of all exam topics. It is an excellent resource for anyone preparing for the AWS Certified DevOps Engineer - Professional exam.
While not solely focused on extraction, this book provides foundational knowledge on data systems, including how data is stored, processed, and moved. Understanding these concepts is crucial for anyone involved in designing and implementing efficient data extraction processes, especially at scale. It's considered a must-read for data professionals.
Instructs data scientists on applied model evaluation, cross-validation, hyperparameter tuning, and pipeline construction through Scikit-Learn maintainers Andreas Müller and Sarah Guido.
Provides a deep dive into the statistical foundations of natural language processing. It valuable resource for anyone who wants to learn about the theory behind text analysis techniques.
Provides a comprehensive overview of information extraction techniques. It covers a wide range of topics, including entity extraction. This book great choice for anyone who wants to learn about information extraction.
Covers the latest AWS certification exam blueprint and provides comprehensive coverage of all exam topics. It is an excellent resource for anyone preparing for the AWS Certified Solutions Architect - Professional exam.
Provides a comprehensive overview of natural language processing techniques, including entity extraction. It is written in Python and includes many practical examples. Accompanying source code and data are available online.
This handbook provides a comprehensive overview of the field of computational linguistics. It valuable resource for anyone who wants to learn about the latest advances in text analysis.
Focuses on AI-based sentiment analysis methods, providing detailed technical coverage and real-world case studies. It aids in developing practical AI-based sentiment analysis systems and includes concepts, technical explanations, and applications.
Covers techniques for mining large datasets, which often involves significant data extraction and processing challenges. It delves into algorithms and methods for handling massive amounts of data, relevant for those working with big data.
Focuses on building NLP applications using Python and popular libraries. It covers practical techniques and real-world problems, which is highly relevant for understanding the capabilities and applications of services like Amazon Comprehend. The second edition includes state-of-the-art models and frameworks.
Investigates the engineering realities of running automated ML pipelines in production, addressing data drift detection, automated retraining loops, telemetry infrastructure, and continuous validation.
Details cloud-native DevOps architectures for orchestrating automated machine learning lifecycles, integrating CI/CD triggers, model registry synchronization, and automated hyperparameter sweep workers.
SQL fundamental language for extracting data from relational databases. focuses on using SQL for data analysis, including techniques for transforming and preparing data. It's valuable for anyone who needs to extract and work with data stored in databases.
Save
Offers a practical approach to text analysis using Python libraries, relevant for implementing sentiment analysis systems. It focuses on applying machine learning to text data and building data products.
Guides engineers step-by-step through implementing, training, and fine-tuning complete generative transformer architectures in pure PyTorch, providing crystal-clear low-level insight into modern models.
Explores building data pipelines for real-time data processing. It is relevant for understanding the challenges and techniques involved in extracting and processing data as it is generated, a key aspect of contemporary data architectures.
Focuses on building practical NLP applications using Python and popular libraries. It includes creating a sentiment analyzer as one of its examples, making it directly relevant for those who want to implement sentiment analysis systems.
Data cleaning critical step after data extraction. provides a comprehensive overview of data cleaning techniques, including error detection and repair. It's an essential resource for ensuring the quality and reliability of extracted data.
Provides a comprehensive overview of deep learning for NLP, covering topics such as word embeddings, recurrent neural networks, and transformers. It is an essential read for anyone interested in this field.
Presents the foundational treatise on Markov decision processes, temporal-difference learning, and policy gradient methods that underpin autonomous decision-making and modern post-training alignment.
Another valuable resource focused on data cleaning, this book provides practical guidance and best practices for preparing data. It is particularly useful for ensuring the quality of data obtained through extraction.
Combines interactive Jupyter notebooks with rigorous mathematical explanations to teach machine learning practitioners how modern neural architectures are designed, implemented, and trained.
Focuses on the practical aspects of data wrangling using Python, including gathering, cleaning, and transforming data. It's a hands-on guide that complements data extraction by showing how to make the extracted data usable.
Provides a comprehensive overview of natural language processing with PyTorch, including a chapter on language detection. It is written by leading researchers in the field, making it a valuable resource for anyone interested in using PyTorch for language detection.
Provides a comprehensive overview of NLP in C++, covering topics such as text processing, machine learning, and deep learning. It is an excellent resource for students and researchers interested in using C++ for their NLP projects.
Unifies computer science, evolutionary biology, and neuroscience to synthesize the five foundational tribes of machine learning, giving practitioners a panoramic interdisciplinary view of learning algorithms.
Provides a comprehensive overview of AWS security best practices and covers topics such as identity and access management, data protection, and network security. It valuable resource for anyone looking to secure their AWS environment.
Provides a practical guide to ETL processes using Microsoft SQL Server Integration Services (SSIS). It covers extracting, transforming, and loading data from various database sources, making it highly relevant for those working with SSIS.
Provides a foundational understanding of data science concepts, including data mining and analytical thinking. It helps in understanding the purpose and value of data extraction within a business context and how extracted data is used for decision-making.
Understanding database management principles is crucial for efficient data extraction from structured sources. covers storing, managing, and analyzing data in databases, providing essential background knowledge.
This classic textbook provides a strong theoretical foundation in statistical methods for NLP. While published some time ago, many of the core concepts are still relevant for understanding the statistical underpinnings of sentiment analysis techniques. It is more valuable as a foundational reference than for contemporary methods.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Getting Started with Amazon Comprehend.
Explore insights from text analysis using Amazon...
Most relevant
Data Engineering in AWS
Most relevant
Explore insights in text analysis using Azure Text...
Most relevant
Introduction to Robotics on AWS
Most relevant
AWS: IoT, Machine Learning and Blockchain
Most relevant
Getting Started with Amazon Personalize
Most relevant
Foundations for Data Analytics Part 2
Most relevant
Chatbots
Most relevant
Introduction to Generative AI for Software Development
Most relevant

Coursera Buyer Protection & Guarantee

Verified partner policies & purchase protections

Accredited University Partner
Free Trial & Audit

7-Day Free Trial & Free Audit

7-day free trial on monthly Coursera Plus subscriptions; free audit option available on most individual courses to explore video lectures and readings at no charge.

Return & Refund Policy

14-Day Refund Guarantee

14-day refund window on annual Coursera Plus subscriptions and single-course purchases prior to certificate issuance. Monthly subscriptions do not offer refunds once billed.

Buyer Protections

Verified Academic & Industry Partner

Accredited university and industry certifications (e.g. Stanford, Google, IBM, Yale), secure SSL payment processing, and verifiable digital certificate links.

Key Purchase Protections
14-day refund window on annual Coursera Plus and single courses
7-day free trial on monthly Coursera Plus subscriptions
Free course audit option for lectures and readings
Verified certificates from top universities & companies
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser