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Mastering Natural Language Processing (NLP) with Deep Learning

Alper Tellioglu

Dive into text data analysis, preprocessing, word embeddings, sequence models and transformers. This course will teach you how to use the power of NLP with deep learning techniques.

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Dive into text data analysis, preprocessing, word embeddings, sequence models and transformers. This course will teach you how to use the power of NLP with deep learning techniques.

Understanding the complex world of natural language processing can be a challenging but essential task in this rapidly growing industry.

In this course, Mastering Natural Language Processing (NLP) with Deep Learning, you’ll gain the ability to analyze text data using advanced deep learning techniques.

First, you’ll explore the essentials of NLP, including the unique challenges of text data and how to preprocess it effectively for various NLP tasks.

Next, you’ll discover the concept of neural networks and their applications in NLP, learning about sequence models and recurrent neural networks (RNNs).

Finally, you’ll learn how to implement and fine-tune transformers such as BERT and GPT for real-world NLP applications.

When you’re finished with this course, you’ll have the skills and knowledge of NLP with deep learning needed to solve complex language processing tasks and build innovative AI solutions.

Enroll now

What's inside

Syllabus

Course Overview
Natural Language Processing (NLP) Essentials: Exploring Text Data
NLP Architectures, Sequence Models, and Transformers

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers fundamentals of text data analysis, which is the cornerstone of NLP tasks
Helps students master preprocessing techniques, the essential step before training NLP models
Demonstrates the use of word embeddings, an advanced NLP technique that captures the semantic meaning of words
Teaches students how to apply sequence models and transformers, core architectures in NLP
Provides hands-on experience with implementing and fine-tuning transformers, empowering students with practical NLP skills

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Activities

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Career center

Learners who complete Mastering Natural Language Processing (NLP) with Deep Learning will develop knowledge and skills that may be useful to these careers:
NLP Engineer
With a focus on building and maintaining NLP systems for various applications, NLP Engineers are responsible for the entire NLP pipeline, from data collection and preprocessing to model development and deployment. A course like Mastering Natural Language Processing with Deep Learning can provide NLP Engineers with in-depth knowledge and practical experience in the field.
NLP Researcher
NLP Researchers conduct research in the field of natural language processing, developing new algorithms and techniques to improve the accuracy and efficiency of NLP systems. A course on Mastering Natural Language Processing with Deep Learning provides NLP Researchers with a solid foundation in the latest advancements in the field, allowing them to contribute to the development of cutting-edge NLP technologies.
Natural Language Processing Scientist
NLP Scientists conduct research and develop new methods and techniques for natural language processing. A course like Mastering Natural Language Processing with Deep Learning can provide NLP Scientists with the knowledge and skills to conduct cutting-edge research in the field.
Text Mining Engineer
Text Mining Engineers use natural language processing techniques to extract useful information from unstructured text data. A course on Mastering Natural Language Processing with Deep Learning can provide Text Mining Engineers with the skills to develop and apply NLP algorithms for various text mining tasks, such as text classification, sentiment analysis, and topic modeling.
Computational Linguist
Computational Linguists study the computational aspects of human language, developing mathematical models and algorithms to understand and process natural language. A course on Mastering Natural Language Processing with Deep Learning can provide Computational Linguists with a solid foundation in the latest NLP techniques, enabling them to contribute to the development of new language technologies.
Information Retrieval Engineer
Information Retrieval Engineers design and implement systems for searching and retrieving information from large collections of data. As NLP techniques are increasingly used in search engines and other information retrieval systems, a course like Mastering Natural Language Processing with Deep Learning can provide Information Retrieval Engineers with the necessary skills to develop and improve these systems.
Technical Writer
Technical Writers create and maintain technical documentation for software, hardware, and other products. As NLP techniques are increasingly used to improve the quality and accessibility of technical documentation, a course on Mastering Natural Language Processing with Deep Learning can be beneficial for Technical Writers who want to specialize in NLP-based documentation.
Knowledge Engineer
Knowledge Engineers design and build knowledge-based systems that can reason and solve problems. A course on Mastering Natural Language Processing with Deep Learning can provide Knowledge Engineers with the skills to develop NLP-based knowledge-based systems that can understand and process natural language.
Linguistic Data Analyst
Linguistic Data Analysts collect, analyze, and interpret linguistic data to gain insights into language patterns and usage. A course on Mastering Natural Language Processing with Deep Learning can provide Linguistic Data Analysts with the computational skills to analyze large volumes of text data and extract meaningful insights.
Information Architect
Information Architects design and organize information systems to ensure that they are easy to find and use. A course on Mastering Natural Language Processing with Deep Learning can provide Information Architects with the skills to design and implement NLP-based information retrieval systems.
Machine Learning Engineer
Machine Learning Engineers research, design, build, test, deploy, monitor, and maintain machine learning models. As NLP becomes ubiquitous in a wide array of software and services, Machine Learning Engineers can build a foundation for a specialization in NLP with a course like Mastering Natural Language Processing with Deep Learning.
Product Manager
Product Managers are responsible for planning, developing, and launching new products or features. With the growing adoption of NLP in product development, a course on Mastering Natural Language Processing with Deep Learning can be beneficial for Product Managers who want to incorporate NLP-based features into their products.
Software Developer
Software Developers design, develop, test, and maintain software applications. Given the increasing use of NLP in software development, a course on Mastering Natural Language Processing with Deep Learning can be beneficial for Software Developers who want to specialize in building NLP-based applications.
Business Analyst
Business Analysts use data and analysis to identify and solve business problems. A course on Mastering Natural Language Processing with Deep Learning can provide Business Analysts with the skills to analyze text data and extract insights that can help businesses make better decisions.
Data Scientist
For individuals interested in using code to find solutions to complex business problems, Data Scientists build and implement predictive models for tasks such as customer segmentation, fraud detection, and forecasting. Given the prominence of natural language processing in various domains, a course on Mastering Natural Language Processing with Deep Learning may be useful for a Data Scientist who wants to specialize in analyzing textual data or developing NLP-based solutions.

Reading list

We've selected eight 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 Mastering Natural Language Processing (NLP) with Deep Learning.
Provides a comprehensive overview of the field of natural language processing, with a focus on deep learning techniques. It covers a wide range of topics, including text preprocessing, word embeddings, sequence models, and transformers.
Classic textbook on speech and language processing. It covers a wide range of topics, including phonetics, phonology, morphology, syntax, semantics, and pragmatics.
Provides a comprehensive overview of deep learning techniques for NLP. It covers topics such as neural networks, recurrent neural networks, and convolutional neural networks, and their applications in various NLP tasks.
Provides a comprehensive overview of the field of natural language understanding. It covers a wide range of topics, including text preprocessing, word embeddings, sequence models, and transformers.
Offers a comprehensive introduction to machine learning techniques for NLP. It covers topics such as text classification, text clustering, and information extraction, and provides a good overview of the field.
This comprehensive textbook provides an introduction to speech and natural language processing, covering topics such as speech recognition, language modeling, and computational linguistics. It offers a strong foundation for understanding the core concepts and challenges in NLP.
Provides a practical introduction to natural language processing with Go, a popular programming language for building microservices, web applications, and cloud-native applications.

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