We may earn an affiliate commission when you visit our partners.
Packt - Course Instructors
This course now features Coursera Coach!
A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.
Read more
This course now features Coursera Coach!
A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.
Read more
This course now features Coursera Coach!
A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.
Unlock the power of natural language processing (NLP) with machine learning techniques using Python in this hands-on, application-focused course. You'll gain practical skills in text classification, sentiment analysis, summarization, and topic modeling—all essential tools in the NLP toolkit. By the end of the course, you'll not only understand key algorithms but also be able to implement them confidently in Python.
The course begins with setup instructions and success tips to ensure a smooth learning experience. You'll dive into spam detection using Naive Bayes, addressing real-world problems like class imbalance and model evaluation with ROC, AUC, and F1 Score metrics. With guided exercises and code demonstrations, you'll learn to build functional spam filters.
Next, you'll explore sentiment analysis through logistic regression, mastering both binary and multiclass classification. Then, you’ll move into text summarization—starting with vector-based approaches and progressing to advanced techniques like TextRank. Both beginner and advanced methods are covered, ensuring an inclusive learning path.
Finally, you'll delve into topic modeling and latent semantic analysis (LSA), implementing algorithms like LDA and NMF in Python. The course is ideal for aspiring data scientists, software engineers, and analysts with basic Python knowledge who want to specialize in NLP. The level is intermediate, and some prior experience in machine learning will help but it is not mandatory.
Register for this course and see more details by visiting:
OpenCourser.com/course/4qfirc/nlp
Here's a deal for you
Save money when you learn with a deal that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Get offer
What's inside
Syllabus
Welcome
In this module, we will introduce you to the course and what lies ahead. You’ll gain a clear understanding of the course roadmap and the unique value it offers. We’ll also share a special offer exclusively for enrolled students.
Read more
Syllabus
Welcome
In this module, we will introduce you to the course and what lies ahead. You’ll gain a clear understanding of the course roadmap and the unique value it offers. We’ll also share a special offer exclusively for enrolled students.
Read more
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Activities
Coming soon
We're preparing activities for
NLP – Machine Learning Models in Python.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete NLP – Machine Learning Models in Python will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer designs, develops, and deploys systems that allow computers to understand, interpret, and generate human language. This role involves building models for various text-based applications, from chatbots to information extraction. The "NLP – Machine Learning Models in Python" course directly prepares learners for this career by providing comprehensive, hands-on experience in essential NLP techniques. Through practical Python implementations, you will gain proficiency in text classification for tasks like spam detection using Naive Bayes, and sentiment analysis with logistic regression. The course further explores advanced methods such as TextRank for text summarization, and LDA and NMF for topic modeling, alongside latent semantic analysis. These foundational skills are crucial for an NLP Engineer building robust language-aware systems, making this course an ideal pathway for those seeking to specialize in this dynamic field.
Text Mining Specialist
Text Mining Specialist
A Text Mining Specialist extracts valuable, actionable insights from large volumes of unstructured text data. This involves using various computational techniques to identify patterns, themes, and relationships within documents. The "NLP – Machine Learning Models in Python" course is exceptionally well-suited for individuals aiming to become a Text Mining Specialist. It provides hands-on experience with the core methodologies required for this role. Learners will gain practical skills in text classification, sentiment analysis, and crucial techniques like text summarization using both vector-based approaches and TextRank. Furthermore, the course deeply explores topic modeling with LDA and NMF, and latent semantic analysis, all implemented in Python. These specific skills are directly applicable to the daily tasks of a Text Mining Specialist, enabling them to uncover hidden information and trends within textual datasets effectively.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer focuses on building, deploying, and maintaining scalable machine learning systems across various domains. This involves selecting appropriate algorithms, optimizing models, and integrating them into production environments. The "NLP – Machine Learning Models in Python" course provides a specialized lens into a critical application area for machine learning engineers: natural language processing. Learners will apply core machine learning algorithms such as Naive Bayes for spam detection and logistic regression for sentiment analysis, gaining practical experience in model evaluation using metrics like ROC, AUC, and F1 Score. The course's emphasis on Python implementation for techniques like text summarization, topic modeling, and latent semantic analysis helps build a strong foundation in applying machine learning to real-world text data, which is highly beneficial for any aspiring Machine Learning Engineer.
Software Engineer (Machine Learning)
Software Engineer (Machine Learning)
A Software Engineer Machine Learning specializes in designing, building, and maintaining the software infrastructure and applications that incorporate machine learning models. This role bridges the gap between machine learning research and scalable production systems. The "NLP – Machine Learning Models in Python" course is highly relevant for Software Engineers looking to specialize in machine learning, particularly within the domain of natural language processing. The course's strong emphasis on practical Python implementation of algorithms is key. You will learn to build functional systems for spam detection using Naive Bayes and sentiment analysis with logistic regression. Furthermore, gaining expertise in implementing text summarization, topic modeling, and latent semantic analysis in Python directly translates into the ability to develop robust, production-ready NLP components, making this course an excellent choice for advancing as a Software Engineer Machine Learning.
Data Scientist
Data Scientist
Data Scientists are experts in extracting insights from complex datasets, often using statistical methods, machine learning, and programming. They analyze data to discover patterns, build predictive models, and communicate findings to inform strategic decisions. The "NLP – Machine Learning Models in Python" course is a valuable asset for aspiring Data Scientists, particularly those aiming to work with unstructured text data. This course equips learners with practical skills in text classification, sentiment analysis, summarization, and topic modeling using Python. Understanding algorithms like Naive Bayes, logistic regression, LDA, and NMF, along with their practical implementation, enhances a Data Scientist's ability to process and derive meaning from vast amounts of textual information, from customer feedback to social media trends. This specialization can significantly broaden a Data Scientist's analytical capabilities.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer develops and implements AI models and algorithms to solve complex problems across various industries. This role involves working with cutting-edge technologies to create intelligent systems that can learn, reason, and adapt. The "NLP – Machine Learning Models in Python" course is highly relevant for an Artificial Intelligence Engineer, as natural language processing is a foundational pillar of modern AI. By mastering text classification, sentiment analysis, summarization, and topic modeling through practical Python exercises, learners will be able to design and build AI applications that interact with and understand human language. The course's coverage of techniques like Naive Bayes, logistic regression, LDA, NMF, and Latent Semantic Analysis provides essential tools for developing intelligent agents and systems that process and interpret textual data, making it a strong foundational course for this career path.
Research Scientist, Artificial Intelligence
Research Scientist, Artificial Intelligence
A Research Scientist Artificial Intelligence explores and develops novel AI algorithms and methodologies, often pushing the boundaries of current technology. This role typically involves significant theoretical understanding, experimentation, and publication of findings, often within an academic or corporate research setting. This career path often requires an advanced degree. The "NLP – Machine Learning Models in Python" course provides a robust practical foundation for an aspiring Research Scientist Artificial Intelligence, particularly those focusing on natural language processing. While research often delves into advanced theoretical concepts, this course equips learners with practical skills in implementing core NLP techniques like text classification, sentiment analysis, summarization, and topic modeling using Python. Understanding the practical application and implementation of algorithms such as Naive Bayes, logistic regression, LDA, NMF, and latent semantic analysis is a valuable complement to theoretical knowledge and can aid in prototyping and validating new ideas.
Customer Experience Analyst
Customer Experience Analyst
A Customer Experience Analyst focuses on understanding and improving the overall journey and satisfaction of customers. This often involves analyzing customer feedback, interactions, and behavioral data to identify pain points and opportunities for enhancement. The "NLP – Machine Learning Models in Python" course provides directly applicable skills for a Customer Experience Analyst. The course's in-depth exploration of sentiment analysis using logistic regression in Python is particularly valuable, enabling you to automatically gauge the emotional tone of customer reviews, survey responses, and support tickets. Additionally, topic modeling with LDA and NMF can help you uncover prevalent themes and emerging issues from vast text datasets of customer feedback. These practical NLP techniques empower a Customer Experience Analyst to derive deeper, quantitative insights from unstructured customer data, leading to more targeted improvements.
Content Analyst
Content Analyst
A Content Analyst evaluates and interprets textual content, often across large datasets, to identify trends, categorize information, ensure quality, or inform content strategy. This role is crucial for organizations dealing with extensive digital text, from articles to user-generated content. The "NLP – Machine Learning Models in Python" course is highly beneficial for a Content Analyst. It provides hands-on Python skills in key NLP areas that directly support content analysis. Learners will master text classification to categorize content, summarization techniques like TextRank to quickly digest large volumes of text, and topic modeling with LDA and NMF to uncover the primary themes and subjects within content repositories. These practical skills enable a Content Analyst to efficiently process, understand, and extract meaningful insights from vast amounts of textual data, improving content management and strategic planning.
Data Analyst
Data Analyst
Data Analysts collect, process, and perform statistical analyses on data to provide actionable insights for businesses. They translate complex data into understandable reports and visualizations, helping organizations make informed decisions. The "NLP – Machine Learning Models in Python" course is a significant advantage for Data Analysts seeking to expand their capabilities beyond structured data. As a Data Analyst, the ability to analyze unstructured text data is increasingly valuable. This course equips you with practical Python skills for sentiment analysis, helping to gauge customer opinions from reviews or social media. It also teaches topic modeling to uncover underlying themes in large text datasets, and text summarization to quickly grasp the essence of documents. These advanced NLP techniques can provide richer, deeper insights that traditional data analysis might miss, empowering Data Analysts to deliver more comprehensive reports.
Computational Linguist
Computational Linguist
A Computational Linguist applies computational methods to problems in language, blending expertise in linguistics with computer science. They often work on developing systems for natural language understanding, machine translation, or speech recognition. This career path often requires an advanced degree. The "NLP – Machine Learning Models in Python" course is beneficial for a Computational Linguist. While the course is application-focused rather than theoretical linguistics, it provides essential practical skills in machine learning models for natural language processing. Learners will implement text classification, sentiment analysis, text summarization, and topic modeling in Python, using algorithms like Naive Bayes, logistic regression, TextRank, LDA, NMF, and latent semantic analysis. These hands-on capabilities are invaluable for building prototypes, processing linguistic data, and evaluating the performance of computational language models, complementing a strong theoretical linguistic background.
Search Engineer
Search Engineer
A Search Engineer designs, develops, and optimizes search engines and information retrieval systems, ensuring users can find relevant information quickly and efficiently. This involves working with indexing, ranking algorithms, and query processing. The "NLP – Machine Learning Models in Python" course is helpful for a Search Engineer, as robust search systems heavily rely on natural language processing to understand user queries and document content. The course's coverage of text classification can help categorize documents for better indexing, while latent semantic analysis (LSA) and topic modeling with LDA and NMF are crucial for understanding the semantic relationships between terms and queries, thereby improving search relevance. The practical Python implementation skills gained will enable a Search Engineer to build and refine components that enhance the intelligence and effectiveness of search functionalities.
Legal Technology Specialist
Legal Technology Specialist
A Legal Technology Specialist applies technology solutions to streamline legal processes, improve efficiency, and analyze vast amounts of legal data. This can involve anything from e-discovery tools to contract review platforms. The "NLP – Machine Learning Models in Python" course may be useful for a Legal Technology Specialist wishing to leverage advanced text analytics. Legal documents are rich in unstructured text, and the course's practical skills in text classification can help categorize contracts or case files. Sentiment analysis could gauge public opinion on legal matters, while topic modeling with LDA and NMF and latent semantic analysis are invaluable for uncovering key themes and hidden patterns in large legal datasets, such as during litigation or regulatory review. Python implementation of these techniques can empower specialists to build custom tools for legal text processing.
Technical Writer Artificial Intelligence
Technical Writer Artificial Intelligence
A Technical Writer Artificial Intelligence creates clear, concise, and accurate documentation for AI products, APIs, and complex machine learning concepts. This role requires understanding intricate technical details and translating them into user-friendly guides, manuals, and tutorials. The "NLP – Machine Learning Models in Python" course may be useful for a Technical Writer specializing in Artificial Intelligence, particularly in the NLP domain. While this role is not directly about implementation, understanding the underlying principles and practical applications of NLP is essential for effective communication. The course provides a hands-on perspective on text classification, sentiment analysis, summarization, and topic modeling using Python. This foundational knowledge of how algorithms like Naive Bayes, logistic regression, LDA, NMF, and latent semantic analysis work and are implemented can significantly enhance a Technical Writer's ability to accurately and confidently document complex NLP systems and models.
Compliance Analyst
Compliance Analyst
A Compliance Analyst ensures that an organization adheres to external laws, regulations, and internal policies. This often involves reviewing vast amounts of textual data, including legal documents, internal communications, and customer interactions, to identify potential risks or non-compliance. The "NLP – Machine Learning Models in Python" course may be useful for a Compliance Analyst aiming to enhance their analytical capabilities with unstructured text. The course provides practical skills in text classification, which can help categorize regulatory documents or internal policies. Sentiment analysis can flag potentially problematic customer communications, while topic modeling with LDA and NMF and latent semantic analysis are powerful tools for identifying key themes and risks across large volumes of compliance-related text, allowing for proactive risk management and efficient review processes using Python.
For more career information including salaries, visit:
OpenCourser.com/course/4qfirc/nlp
Reading list
We've selected 22 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 – Machine Learning Models in Python.
Serves as a comprehensive companion to the course, covering nearly every syllabus topic including sentiment analysis, text summarization, and topic modeling. It is highly valuable as a current reference for implementing NLTK, Gensim, and Scikit-Learn in Python. Industry professionals frequently use this text to bridge the gap between theoretical NLP and practical application. It adds significant depth to the course's sections on Latent Semantic Analysis and advanced text classification.
Is an excellent practical guide that mirrors the course's focus on building machine learning pipelines for text. It is particularly helpful for providing background knowledge on feature engineering and vectorization techniques like TF-IDF. It is commonly used by data scientists as a reference tool for Scikit-Learn based NLP workflows. The text adds breadth by exploring how to scale these models within a production environment.
Provides a deep dive into the 'classic' NLP stack, including detailed chapters on LSA and Word2Vec that supplement the course's module on Latent Semantic Analysis. It is an ideal textbook for learners who want to understand the mathematical intuition behind the algorithms. The authors are recognized authorities who explain complex topics like summarization and topic modeling with clarity. It is more valuable as a comprehensive study guide than a quick syntax reference.
This recent publication is essential for learners who want to see how the course's models are applied in real-world business contexts. It provides prerequisite knowledge on the NLP pipeline and expands on the course's spam detection and sentiment analysis modules. It is widely regarded as a modern industry standard for building production-ready NLP systems. The book adds breadth by discussing data collection and deployment strategies not covered in the course.
While not exclusively an NLP book, this is the definitive reference for the machine learning models used in the course, such as Naive Bayes and Logistic Regression. It is extremely helpful for providing the foundational ML background required to succeed in the course's technical modules. This standard textbook at many academic institutions and is updated frequently to remain relevant. It adds depth to the course's evaluation metrics section, specifically ROC and AUC curves.
Provides project-based blueprints that align perfectly with the course's hands-on application focus. It useful reference tool for learners looking to implement topic modeling and text summarization on large datasets. The book is published recently and covers modern Python libraries that complement the course syllabus. It adds more breadth by showing how to handle messy, real-world text data before modeling.
Often referred to as the 'Bible' of NLP, this book provides the deep theoretical and academic background for every concept in the course. It is the most authoritative textbook in the field and is used globally in university curricula. While it is more dense than the course, it offers unparalleled depth on the mechanics of Naive Bayes and Logistic Regression. It is best used as a permanent reference for the underlying science of natural language.
Serves as the perfect 'next step' for students who have completed the course and want to move from classic ML to state-of-the-art Deep Learning. It provides a modern perspective on text summarization and sentiment analysis using Hugging Face. The authors are leading authorities in the modern NLP landscape. Reading this adds significant breadth by introducing the Transformer architecture which succeeds the models taught in the course.
Is highly reputable for its clear explanation of the algorithms mentioned in the course, specifically Logistic Regression and Naive Bayes. It is an excellent supplementary text for understanding the optimization techniques used during model training. It is commonly used by industry professionals as a comprehensive guide to Python's ML ecosystem. The book adds depth to the course's discussion on model evaluation and class imbalance.
Is particularly useful for the course's modules on Latent Semantic Analysis and vector-based summarization. It provides the rigorous mathematical background for the Vector Space Model and Singular Value Decomposition. While older than some other entries, it remains a foundational academic textbook. It vital reference for understanding how search engines and summarizers rank and retrieve text.
Is excellent for learners who want to see the course's concepts applied to real-world software engineering problems. It provides prerequisite context on how to build NLP applications that are robust and scalable. The author recognized expert, and the book is published within the last few years. It adds breadth by covering multilingual NLP and the practicalities of data annotation.
Direct supplement to the course's Python-heavy approach, covering NLTK, Spacy, and Gensim. It is particularly helpful for the topic modeling and sentiment analysis sections of the syllabus. It is designed for practitioners, making it a useful reference tool for coding exercises. It reinforces the course material by providing alternative code demonstrations for similar NLP tasks.
Is essential for providing background knowledge on how text is transformed into numbers, which core part of the course. It offers a deep dive into Bag-of-Words, TF-IDF, and N-grams. It highly regarded reference for improving model performance through better data representation. It adds depth to the course's initial modules on spam detection and text classification.
Written by the creator of the Pandas library, this book provides the absolute prerequisite knowledge for handling datasets in the course. It is essential for learning how to manipulate the CSV and text files used in the spam detection and sentiment analysis modules. This is the industry-standard textbook for data manipulation in Python. It necessary reference for any student who feels their Python data skills are lacking.
This academic textbook provides a comprehensive and mathematically rigorous treatment of text mining and NLP. It is especially strong in its coverage of Latent Semantic Analysis and Topic Modeling, which are key course components. It is best used as a deep-dive reference for students who want to understand the 'why' behind the algorithms. It adds significant theoretical breadth to the course's practical implementations.
Provides an updated look at the evolution of NLP models, helping students understand where the course's models fit in history. It valuable additional reading for those interested in the transition from LSA to modern attention-based models. The author provides practical examples using Python and Hugging Face. It adds breadth by discussing the ethical implications of AI and NLP.
Great alternative for students who want to implement the course's concepts using the PyTorch framework instead of Scikit-Learn. It provides a solid foundation in neural networks as applied to text. It well-respected reference in the deep learning community. The book adds depth to the sentiment analysis and text classification modules through the lens of deep learning.
This is the classic NLTK book that many NLP practitioners start with. While some of the libraries have evolved, the foundational concepts of tokenization and part-of-speech tagging are excellent prerequisites for the course. It useful reference tool for basic text processing tasks. It adds historical context and depth to the course's introductory modules.
This classic academic reference that provides the statistical foundations for algorithms like Naive Bayes. It is more valuable as a historical and theoretical reference than as a guide for modern Python coding. It is widely used in graduate-level NLP courses. It adds extreme depth to the course's modules on probability and statistical modeling of text.
Focuses specifically on modern transformer models that represent the next stage of evolution after the course's models. It useful additional reading for students who want to understand how sentiment analysis is performed today using pre-trained models. The book is very recent and focuses on practical Python implementation. It adds breadth to the course's classification and summarization modules.
Offers a deep dive into building NLP models using the TensorFlow framework. It provides prerequisite knowledge on building neural networks for those who want to extend the course's machine learning models. It useful reference for implementing advanced text summarization techniques. It adds depth to the course's modules on topic modeling and classification using deep learning.
Is fantastic for providing the intuitive background knowledge needed for the course's machine learning algorithms. It explains Naive Bayes and Logistic Regression using visual and easy-to-understand analogies. It perfect prerequisite for students who find the course's technical modules a bit too fast-paced. It adds clarity to the fundamental concepts of model evaluation and probability.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4qfirc/nlp
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Level
Intermediate
Via
Coursera
Institution
Packt
Instructor
Packt - Course Instructors
Session
October 27, 2025
- December 22, 2025
Language
English
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