We may earn an affiliate commission when you visit our partners.
Ramin Mohammadi
Welcome to this course on applied natural language processing in engineering. This course is designed to provide you with an in-depth understanding of NLP, a pivotal area of artificial intelligence that empowers computers to comprehend, interpret, and generate human language. Throughout this course, you will explore a wide range of topics, from fundamental NLP tasks like text classification and Named Entity Recognition (NER) to advanced techniques in neural machine translation and optimization methods critical for machine learning. We will delve into the complexities of teaching language to machines, addressing challenges like ambiguity, grammar, and cultural nuances. By the end of this part 1 course, you will have a foundational understanding of how modern NLP systems work - particularly those involving machine learning and deep learning. These topics will equip you to build, analyze and improve NLP systems across many applications.
Register for this course and see more details by visiting:
OpenCourser.com/course/xjqu8p/applied
Two deals to help you save
Save money when you learn. Here are two deals and offers that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until July 14
Coursera Plus Annual
Work toward what's next for your career with access to 10,000+ programs. Discover emerging skills and save.
Up to
40%
off
Get offer
Valid until August 30
Google AI App Builder
Learn how to use Gemini API and API Studio with a three-course series from Google DeepMind
Take
100%
off
Get offer
What's inside
Syllabus
Introduction to Speech, Language, and Natural Language Processing
This module provides an in-depth exploration of Natural Language Processing (NLP), a crucial area of artificial intelligence that enables computers to understand, interpret, and generate human language. By combining computational linguistics with machine learning, NLP is applied in various technologies, from chatbots and sentiment analysis to machine translation and speech recognition. The module introduces fundamental NLP tasks such as text classification, Named Entity Recognition (NER), and neural machine translation, showcasing how these applications shape real-world interactions with AI. Additionally, it highlights the complexities of teaching language to machines, including handling ambiguity, grammar, and cultural nuances. Through the course, you will gain hands-on experience and knowledge about key techniques like word representation and distributional semantics, preparing them to solve language-related challenges in modern AI systems.
Read more
Syllabus
Introduction to Speech, Language, and Natural Language Processing
This module provides an in-depth exploration of Natural Language Processing (NLP), a crucial area of artificial intelligence that enables computers to understand, interpret, and generate human language. By combining computational linguistics with machine learning, NLP is applied in various technologies, from chatbots and sentiment analysis to machine translation and speech recognition. The module introduces fundamental NLP tasks such as text classification, Named Entity Recognition (NER), and neural machine translation, showcasing how these applications shape real-world interactions with AI. Additionally, it highlights the complexities of teaching language to machines, including handling ambiguity, grammar, and cultural nuances. Through the course, you will gain hands-on experience and knowledge about key techniques like word representation and distributional semantics, preparing them to solve language-related challenges in modern AI systems.
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
Applied Natural Language Processing in Engineering Part 1.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Applied Natural Language Processing in Engineering Part 1 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 process and understand human language. This course provides an in-depth understanding of NLP, equipping learners to build, analyze, and improve such systems. You will gain foundational knowledge of modern NLP, machine learning, and deep learning, crucial for tackling challenges like text classification, Named Entity Recognition, and neural machine translation. The course's focus on optimization techniques and advanced models like transformers helps develop the skills to create accurate and efficient NLP applications, making it essential for anyone pursuing this specialized engineering role.
Applied Scientist Natural Language Processing
Applied Scientist Natural Language Processing
An Applied Scientist Natural Language Processing researches and develops innovative NLP solutions, bridging cutting-edge research with practical applications. This role often requires an advanced degree. The course provides an in-depth understanding of applied natural language processing in engineering, equipping learners with the capacity to build, analyze, and improve complex NLP systems. Learners will explore fundamental NLP tasks, advanced deep learning techniques, and critical evaluation methods. This comprehensive foundation helps one translate theoretical understanding into impactful, scalable solutions, making the course highly beneficial for an aspiring Applied Scientist Natural Language Processing.
Natural Language Understanding Specialist
Natural Language Understanding Specialist
A Natural Language Understanding Specialist develops systems that enable computers to deeply comprehend and interpret human language, focusing on the nuances of meaning and intent. This course is highly relevant, providing an in-depth understanding of NLP, a pivotal area of artificial intelligence that empowers computers to comprehend and interpret human language. The exploration of ambiguity, grammar, cultural nuances, and semantic parsing within the course directly addresses the core challenges faced by a Natural Language Understanding Specialist. Additionally, the foundational understanding of machine learning and deep learning helps build robust and sophisticated NLU models.
Information Extraction Engineer
Information Extraction Engineer
An Information Extraction Engineer builds systems that automatically identify and extract structured data from unstructured text, turning free-form language into actionable insights. This course is highly relevant, delving into core NLP tasks like Named Entity Recognition, which is fundamental to information extraction. It also covers dependency parsing and semantic parsing, emphasizing their role in mapping sentences to formal representations of meaning. These specific skills, combined with an understanding of neural networks and deep learning for handling complex patterns, are precisely what an Information Extraction Engineer needs to design, implement, and improve robust extraction pipelines.
Applied Data Scientist Natural Language Processing
Applied Data Scientist Natural Language Processing
An Applied Data Scientist Natural Language Processing focuses on leveraging NLP techniques to extract insights, build predictive models, and drive data-driven strategies specifically from textual data. This course provides an in-depth understanding of applied natural language processing, equipping learners to build, analyze, and improve NLP systems across many applications. Modules like text classification, Named Entity Recognition, topic modeling, and detailed evaluation techniques are directly applicable to solving real-world business problems. The foundational understanding of machine learning and deep learning for language tasks makes this course highly relevant for an Applied Data Scientist Natural Language Processing.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer focuses on building, deploying, and maintaining machine learning models and infrastructure. This course helps develop a foundational understanding of modern NLP systems, particularly those involving machine learning and deep learning, which are critical skills for a Machine Learning Engineer. The modules on optimization techniques like Gradient Descent, neural networks, and advanced models such as Recurrent Neural Networks and transformers provide practical knowledge for developing robust ML solutions. Understanding how to evaluate model performance and address real-world challenges makes this course highly relevant for engineering sophisticated ML applications.
Text Mining Specialist
Text Mining Specialist
A Text Mining Specialist extracts meaningful patterns, insights, and knowledge from large volumes of unstructured textual data. This course is highly beneficial for a Text Mining Specialist, offering a deep dive into fundamental NLP tasks like text classification and Named Entity Recognition. Crucially, it includes a dedicated module on topic modeling, covering techniques such as Latent Semantic Analysis, Non-Negative Matrix Factorization, and Latent Dirichlet Allocation, directly applicable for uncovering hidden themes in text. The emphasis on transforming textual data into numerical representations and using evaluation techniques further strengthens one's ability to analyze and interpret text effectively.
Deep Learning Engineer
Deep Learning Engineer
A Deep Learning Engineer specializes in designing, training, and deploying neural network models for various artificial intelligence tasks. The course helps build a foundational understanding of modern NLP systems, particularly those involving machine learning and deep learning, which are central to the work of a Deep Learning Engineer. Exploring neural networks, Recurrent Neural Networks, and transformers like BERT provides direct, applicable knowledge. Furthermore, the detailed modules on gradient descent, optimization techniques, and evaluation methods are invaluable for developing high-performing and efficient deep learning architectures for language-focused applications.
Chatbot Developer
Chatbot Developer
A Chatbot Developer designs, implements, and refines conversational AI systems that can understand and respond to human interaction. The course provides an invaluable foundation in natural language processing, a pivotal area of artificial intelligence that empowers computers to comprehend, interpret, and generate human language, directly supporting the work of a Chatbot Developer. Learners will explore topics like text classification, Named Entity Recognition, and semantic parsing, all critical for building intelligent dialogue systems. Understanding ambiguity and cultural nuances in language, as addressed in the course, is essential for creating effective and user-friendly chatbots.
Computational Linguist
Computational Linguist
A Computational Linguist applies computational methods to analyze and process human language, often developing tools and models for linguistic phenomena. This role often requires an advanced degree. The course directly addresses the complexities of teaching language to machines, including handling ambiguity, grammar, and cultural nuances, which are core concerns for a Computational Linguist. It also covers essential linguistic structures like phrase and dependency structure, and various parsing methods. This foundational understanding allows one to approach language-related challenges with both linguistic insight and the technical capability to implement advanced NLP models.
Research Scientist, Artificial Intelligence
Research Scientist, Artificial Intelligence
A Research Scientist Artificial Intelligence explores and develops novel AI algorithms and models, pushing the boundaries of what machines can achieve. This role often requires an advanced degree. The course provides a foundational understanding of how modern NLP systems work, particularly those involving machine learning and deep learning, which is a significant area of AI research. Learners delve into advanced techniques like neural machine translation, optimization methods, and the intricacies of models such as RNNs and transformers. The strong emphasis on evaluation techniques helps one rigorously assess new models, preparing them for cutting-edge research in AI.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, builds, and deploys intelligent systems that leverage various AI technologies. The course provides an in-depth understanding of natural language processing, a pivotal area of artificial intelligence that empowers computers to comprehend, interpret, and generate human language. This knowledge is crucial for an Artificial Intelligence Engineer working on applications that involve human-computer interaction, data analysis from text, or automated content generation. The course's coverage of machine learning, deep learning, and optimization techniques helps build robust and efficient AI solutions across many applications.
Data Scientist
Data Scientist
A Data Scientist extracts insights and builds predictive models from complex datasets. The course helps develop a foundational understanding of modern NLP systems, which are increasingly vital for a Data Scientist working with unstructured textual data. Techniques like text classification, Named Entity Recognition, and topic modeling are directly applicable for analyzing customer feedback, social media data, or scientific literature. The understanding of word representations, semantic relationships, and evaluation techniques helps one to preprocess, analyze, and validate textual data, enabling more sophisticated data-driven decision-making.
Software Engineer (Machine Learning)
Software Engineer (Machine Learning)
A Software Engineer Machine Learning integrates machine learning models into larger software systems and ensures their robust and scalable operation. The course helps build, analyze, and improve NLP systems, providing practical skills for a Software Engineer Machine Learning working with language-centric applications. Understanding the foundational principles of machine learning, deep learning, and specific NLP techniques like text classification and neural machine translation, helps in seamlessly integrating these complex models. The focus on optimization and evaluation methods further equips one to deploy efficient and high-performing NLP components within software infrastructures.
Search Engineer
Search Engineer
A Search Engineer designs, implements, and optimizes search engines and information retrieval systems. The course helps develop a foundational understanding of how modern NLP systems work, which is increasingly relevant for improving search relevance and query understanding for a Search Engineer. Topics like word representation, distributional semantics, and semantic similarity (via embeddings like Word2Vec and GloVe) are crucial for enhancing how search queries are processed and how documents are matched. Understanding text classification and evaluation techniques helps in building more intelligent and accurate search experiences.
For more career information including salaries, visit:
OpenCourser.com/course/xjqu8p/applied
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
Applied Natural Language Processing in Engineering Part 1.
Modern practical guide that focuses on the Hugging Face ecosystem, which is the industry standard for applying NLP in engineering. It provides excellent breadth on evaluation techniques like BLEU and ROUGE, as well as hands-on approaches to Named Entity Recognition. It is highly valuable as a current reference for learners who want to move from the theory of Part 1 to real-world implementation.
Aligns perfectly with the 'Applied' and 'Engineering' aspects of the course by focusing on the entire NLP pipeline. It is particularly helpful for understanding the trade-offs between different models and how to evaluate them in production environments. It serves as a great bridge between academic concepts like Word2Vec and actual engineering deployment.
Goldberg leading authority in the field, and this book provides the deep mathematical and structural background needed for the course's modules on Neural Networks and Cost Functions. It offers significant depth into how word embeddings and recurrent structures work under the hood. While slightly older than 5 years, it remains a foundational textbook for understanding the transition from traditional to neural NLP.
Offers a comprehensive and mathematically grounded introduction that is frequently used in graduate-level engineering programs. It is particularly strong in its treatment of dependency parsing and structured prediction, which are key components of the course syllabus. Reading this provides the necessary background for students who find the course's technical modules move too quickly.
Save
This is the primary reference for the course's module on Gradient Descent and Optimization Techniques. It provides an exhaustive look at SGD, Adam, and second-order methods like Newton's Method, which are critical for training NLP models. It is more valuable as a deep-dive reference for the underlying mathematics of machine learning than as a specific NLP guide.
Provides a very accessible introduction to the optimization techniques and neural network basics discussed in the course. It is an excellent resource for prerequisite knowledge, especially for learners who need a refresher on cost functions and gradient descent. The recent third edition ensures the code examples for NLP tasks like text classification are up to date.
Is particularly useful for the course module on Topic Modeling, covering LSA, LDA, and NMF in great detail with Python implementations. It adds breadth to the course by exploring various feature engineering techniques beyond just embeddings. It is commonly used by industry professionals to understand the practicalities of processing large text datasets.
Since many engineering applications of NLP use PyTorch, this book is an excellent companion for the course's neural network modules. It provides clear explanations of how to implement Word2Vec and GloVe from scratch. It serves as a useful reference tool for students who want to see the mathematical concepts of the course translated into clean, modular code.
Focuses on building end-to-end pipelines, making it a great supplement for the 'Applied' portion of the course name. It provides a very intuitive explanation of word vectors and semantic relationships that helps demystify the GloVe and Word2Vec modules. It is highly recommended for learners who prefer a 'code-first' approach to learning complex AI concepts.
This textbook offers a rigorous academic look at text mining and NLP, with significant focus on matrix factorization and topic modeling. It provides the mathematical depth for LSA and NMF that supplements the course's syllabus perfectly. It valuable additional reading for those pursuing the course for academic or research reasons.
This is the authoritative text for the course's module on advanced optimization, specifically covering Quasi-Newton methods like BFGS. While it is not an NLP book, it is the best reference for the second-order techniques mentioned in the syllabus. It high-level reference tool for students who want to understand the convergence properties of the algorithms they use.
Focuses heavily on the Transformer architecture and BERT, which are highlighted in the syllabus's discussion on NER and deep learning. It provides a more industry-oriented perspective on how these models are changing the field. It useful tool for students looking for breadth in modern transformer applications.
Although published some time ago, this classic text by a Stanford authority provides the foundational logic for tasks like dependency parsing and word co-occurrence. It is excellent for background knowledge on the statistical methods that preceded modern deep learning. It is more valuable as a historical and theoretical reference than for current neural techniques.
Emphasizes the engineering workflow, from data collection to model evaluation. It provides practical context for the evaluation metrics module, explaining why certain metrics are chosen for specific engineering goals. It great resource for students who want to understand the 'Engineering' part of the course title.
This is the classic introduction to NLP using the NLTK library. It is helpful for providing prerequisite knowledge on basic NLP tasks like tokenization and part-of-speech tagging that are assumed in the course. It is widely used at academic institutions as an introductory text for those new to the field.
This recent publication focuses on building robust NLP applications, covering many of the neural techniques mentioned in the course. It is particularly good at explaining the practical challenges of ambiguity and grammar in human language. It adds breadth by showing how to handle real-world, messy data which is often glossed over in lectures.
Provides the linguistic background that many engineering students lack, particularly regarding syntax and semantics. It is very helpful for the course module on Dependency Parsing, as it explains the 'why' behind the linguistic structures. It short but dense reference for the linguistic side of NLP.
This collection of papers and chapters provides a deep dive into the optimization algorithms that power modern AI. It specifically addresses the challenges of large-scale datasets, which is relevant to the Mini-Batch Gradient Descent topics in the course. It highly technical supplemental reading for the mathematically inclined student.
Focuses on the architecture of transformers and their application in tasks like NER and machine translation. It aligns well with the syllabus's focus on modern neural techniques and BERT. It useful reference for students wanting to extend their knowledge of the transformer module into specialized applications.
Provides a targeted look at the BERT model, which is mentioned in the course's NER and neural networks modules. It explains the mechanics of the transformer encoder and how to fine-tune it for specific tasks. It is more valuable as additional reading for those specifically interested in the state-of-the-art models discussed at the end of Part 1.
Provides a deep dive into graph-based methods, which are specifically mentioned in the course's module on dependency parsing. It explains how to represent text as graphs and apply algorithms to them. It is an excellent technical reference for the parsing and semantic structure parts of the syllabus.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xjqu8p/applied
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
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